A Study on Combined Low-pass Whole-genome and Methylome Testing of Bloody Nipple Discharge Specimens for Benign-Malignant Differentiation.
Conditions: Breast Cancer, Nipple Discharge
Sponsor: Hubei Cancer Hospital

Cover Page 
Official Title: Combined Low-Coverage Whole-Genome and Methylome Testing of 
Nipple Discharge to Differentiate Benign and Malignant Breast Lesions: A 
Prospective Diagnostic Study 
Department: Breast Surgery 
Institution/Sponsor: Hubei Cancer Hospital 
Study Location: Breast Surgery Department, Hubei Cancer Hospital, Wuhan, China 
NCT Number: [NCTXXXXXXXX] 
Document Type: Study Protocol 
Version: v1.0 
Document Date: 05 September 2025 
 
 
 
 
 
 
 
 
 
 
 

1. Background and Rationale 
Abnormal nipple discharge—especially bloody discharge—is a common clinical 
presentation of breast disease. Etiologies range from benign lesions (e.g., intraductal 
papilloma) to malignancies (e.g., ductal carcinoma in situ, DCIS; or invasive ductal 
carcinoma, IDC). Although most pathologic nipple discharge cases are benign, surgical 
duct excision for histopathology remains the diagnostic gold standard to rule out cancer. 
This strategy leads to a considerable proportion of patients undergoing unnecessary 
surgery. Reported malignancy rates among pathologic nipple discharge cases are 
approximately 11%–16% (range 3%–29% across studies), meaning a majority of 
operated patients are ultimately diagnosed with benign disease. Common benign causes 
include intraductal papilloma (35%–48%) and duct ectasia (17%–36%). Thus, many 
patients experience surgical trauma, anesthesia, and postoperative recovery despite 
benign pathology, creating avoidable physical, psychological, and resource burdens. 
Non- or minimally invasive diagnostic tools for nipple discharge remain limited. 
Conventional cytology of nipple discharge has low diagnostic value, with reported 
sensitivity for breast cancer of 11%–34.6% and a false-negative rate up to 65% [1], 
which precludes reliable rule-out and explains why many guidelines do not recommend 
routine cytology screening [1]. Imaging (ultrasound, mammography, ductography, MRI) 
can help localize ductal lesions but has limited overall sensitivity/specificity and cannot 
definitively distinguish benign from malignant disease without surgical biopsy. There 
is a pressing need for molecular diagnostics that can accurately classify nipple discharge 
as benign or malignant preoperatively. Reliable identification of benign cases would 
avoid unnecessary operations, while timely detection of malignancy would expedite 
appropriate surgery, reducing harm and cost and improving decision-making. 
Liquid biopsy approaches offer new opportunities. Nipple discharge is a ductal fluid 
containing cells and cell-free DNA (cfDNA) proximal to intraductal lesions and may 
be a promising biospecimen for early breast cancer detection [2]. Studies have explored 
markers in nipple aspirate fluid (NAF) for screening and diagnosis [2], including whole-
genome sequencing of cfDNA and targeted DNA methylation assays to aid tumor 

diagnosis [1,3]. DNA methylation is a key epigenetic hallmark of cancer; promoter 
hypermethylation of breast cancer–related genes (e.g., Cyclin D2) is frequent in 
malignancy but uncommon in benign disease [1,3]. In fine-needle aspirates, 
methylation of selected tumor suppressors has achieved high diagnostic specificity (up 
to 100%) for breast cancer [1,3]. Low-coverage whole-genome sequencing (lcWGS) of 
cfDNA can capture tumor-associated genomic instability, such as somatic copy-number 
alterations (CNAs) and aberrant fragmentomic patterns. In plasma studies, lcWGS has 
enabled noninvasive cancer detection, especially in chromosomally unstable tumors, 
via abnormal CNA profiles [4]. Fragmentomics (e.g., cfDNA fragment length 
distributions, end motifs, nucleosome footprints) can further increase sensitivity [4]. 
Combining CNAs with fragment features improves detection versus either alone (by 
~20% in some contexts) [4]. Therefore, integrating genomic (lcWGS) and epigenomic 
(methylome) features may substantially enhance detection of tumor signals in nipple 
discharge. 
This study proposes a minimally invasive diagnostic model that integrates lcWGS- and 
methylation-derived features from nipple discharge to distinguish benign from 
malignant intraductal breast lesions preoperatively. If validated, the model could inform 
surgical decision-making, reduce unnecessary operations in benign disease, and ensure 
timely treatment of malignancy, providing meaningful clinical and societal value. 
 
2. Objectives 
Overall objective:  
Develop and validate a molecular diagnostic model based on nipple discharge to 
discriminate benign versus malignant breast lesions and guide surgical decisions. 
Primary objective: 
Construct a binary classifier integrating lcWGS and DNA methylome features to 
distinguish benign from malignant disease based on nipple discharge, and evaluate 

diagnostic performance (AUC, sensitivity, specificity) via model training and internal 
validation. 
Secondary objectives: 
(1) Feature discovery: Identify malignancy-associated molecular features in nipple 
discharge, including genomic (CNA profiles, cfDNA fragment length distributions, 
other fragmentomics) and methylation markers; determine features enriched or 
elevated in malignancy. 
(2) Model optimization and performance: Explore different feature sets and 
algorithms (e.g., logistic regression, machine learning) to improve robustness; 
determine optimal thresholds via ROC analysis; compute sensitivity, specificity, PPV, 
NPV; compare single-marker versus combined models. 
(3) Independent validation and clinical utility: Test the trained model in an 
independent validation cohort to assess generalizability; estimate potential clinical 
benefit (e.g., proportion of benign surgeries avoided at a high-sensitivity operating 
point). 
 
3. Study Design and Overview 
Design: Single-center, prospective diagnostic accuracy study. 
Setting: Hubei Cancer Hospital, Breast Surgery Department. 
Population: Patients with suspected pathologic nipple discharge scheduled for 
diagnostic surgery; postoperative pathology serves as the reference standard. 
Sample size: Approximately 30 consecutive eligible participants (enriched by routine 
clinical flow), including both benign (e.g., intraductal papilloma, duct ectasia) and 
malignant cases (e.g., DCIS, IDC). 
Enrollment period: September 2025 to May 2026, or until target accrual is reached. 

Study flow: 
  (1) Screening and consent. 
  (2) Preoperative nipple discharge collection (and NAF if needed) and one 
peripheral blood draw (~5 mL, EDTA). 
  (3) Processing, storage, and molecular assays (lcWGS and methylation profiling). 
  (4) Collection of clinical data and postoperative pathology. 
  (5) Feature extraction, model training, and validation. 
 
4. Study Population 
Inclusion criteria 
(1) Age ≥18 years; primarily female (male cases may be considered but are not the 
main recruitment population). 
(2) Unilateral, spontaneous, single-duct abnormal nipple discharge, predominantly 
bloody or serosanguineous (pathologic discharge), recurrent or persistent with clinical 
concern. 
(3) Planned diagnostic surgery (e.g., microdochectomy/duct excision biopsy or tumor 
excision) after specialist evaluation; cases post-ductoscopy still proceeding to surgery 
are eligible. 
(4)Willing and able to provide written informed consent, nipple discharge sample, and 
blood sample; agree to clinical data and postoperative pathology access. 
Exclusion criteria 
(1) Physiologic or nonspecific discharge (e.g., bilateral, multi-duct discharge on 
expression; small amounts of milky/clear/green discharge suggestive of physiologic 
or benign etiologies such as hyperprolactinemia or duct ectasia). 

(2) Discharge due to overt infection/inflammation (e.g., abscess drainage) likely to 
confound DNA analysis. 
(3) Prior diagnosis and treatment of breast cancer (surgery/radiation/chemotherapy) 
that could confound current ductal secretion and genomic characteristics. 
(4) Recent invasive ductal procedures (e.g., ductoscopy with instrumentation, duct 
cannulation/irrigation) that may introduce artifacts before sampling. 
(5) Pregnancy or lactation, due to physiologic breast changes and safety 
considerations. 
(6) Severe hematologic disorders or coagulopathy precluding safe sampling. 
(7) Refusal or withdrawal of consent; subjects later found ineligible will be 
discontinued. 
 
5. Study Procedures and Sample Handling 
Nipple discharge collection 
(1) Timing: Preoperative, after consent. 
(2) Visible discharge: Gently identify the secreting duct orifice with a sterile slide, 
then collect the fluid directly using a sterile swab or microtube. 
(3) If no visible discharge at the visit but history supports pathologic discharge: Use 
nipple aspirate fluid (NAF) collection [2] with a sterile nipple cup and 10 mL syringe 
to apply gentle vacuum for several minutes; light breast massage may be used to 
facilitate flow. Literature-based adjuncts such as brief warm compress or oxytocin 
nasal spray may be considered per institutional policy to increase yield [2]. 
(4) Target volume: ≥100 μL per participant when feasible; any amount obtained will 
be processed using cfDNA workflows if volume is limited. 
(5) Documentation: Record date/time, side, macroscopic appearance 

(bloody/serosanguineous/other), and estimated volume. 
(6) Storage: Process promptly. Short-term 4°C if immediate processing; otherwise 
snap-cool and store at −80°C to minimize DNA degradation. 
Blood collection 
Peripheral blood (~5 mL, EDTA) for germline leukocyte DNA and/or plasma cfDNA 
as reference. 
Sample processing 
(1) If sufficient cellular material is present: Low-speed centrifugation to separate cell 
pellet and supernatant; extract genomic DNA from pellet; concentrate cfDNA from 
supernatant (e.g., filtration or cfDNA kits). 
(2) For low-volume or acellular specimens: Process entire sample with a cfDNA 
extraction kit. 
(3) Label all materials with coded study IDs only. Log preanalytical variables. 
 
6. Outcome Measures 
Primary endpoint 
Diagnostic accuracy of the combined lcWGS + methylome model to discriminate 
benign vs malignant disease in nipple discharge, quantified by area under the ROC 
curve (AUC) with 95% confidence intervals in training and validation analyses. 
Sensitivity and specificity will be emphasized; operating points will prioritize high 
sensitivity. 
Secondary endpoints 
(1) Predictive values: Positive predictive value (PPV) and negative predictive value 
(NPV). 
(2) Calibration: Calibration plots and/or Hosmer–Lemeshow test to assess agreement 

between predicted probabilities and observed outcomes. 
(3) Threshold/clinical impact analyses: Operating point selection (e.g., maximal 
Youden index) and projected clinical effects (e.g., proportion of benign surgeries 
potentially avoided, expected miss rate) under predefined sensitivity constraints (e.g., 
≥95% sensitivity). 
(4) Subgroup performance: Stratified analyses by pathology subtype (DCIS vs IDC), 
tumor size, or imaging findings. 
(5) Safety metrics: False-negative rate (missed malignancy) and characterization of 
any false-negative cases, with root-cause analysis. 
 
7. Laboratory Methods 
Sequencing and quality control 
(1) Library prep and sequencing per standard operating procedures. Generate raw 
FASTQ files. 
(2) Quality control: FastQC or equivalent for read quality, adapters, and error profiles. 
Read alignment 
(1) lcWGS data: Align to GRCh38/hg38 using bwa-mem2; produce BAM files; 
mark/remove PCR duplicates; apply quality filtering. 
(2) Methylation data: For whole-genome bisulfite sequencing (WGBS), align with 
Bismark or equivalent; extract CpG-level methylation metrics. 
Genomic variation (lcWGS) and fragmentomics 
(1) CNA calling from low-depth data using algorithms suited for lcWGS (e.g., 
ichorCNA, Control-FREEC) with read-depth normalization and segmentation [4]. 
(2) Derive tumor-associated CNA features: genome instability indices (e.g., fraction 
of genome altered), recurrent breast cancer–related CNAs (e.g., 8q24 gains), 

presence/absence indicators. 
(3) Fragmentomics: Compute cfDNA fragment length distributions, short-fragment 
fractions, end motif bias, and additional indices (e.g., fragmentation uniformity, TSS-
associated patterns) where feasible [4]. 
Methylation analysis 
(1) Compute site/region-level methylation (beta values). 
(2) Differential methylation analysis between benign and malignant groups to identify 
differentially methylated cytosines (DMCs) and regions (DMRs) using 
R/Bioconductor packages (e.g., limma, DSS); multiple-testing adjustment; example 
thresholds: |Δβ| above preset cutoff with adjusted p<0.01. 
(3) Functional annotation/enrichment for genes overlapping significant DMRs to 
support biological plausibility. 
(4) If sequencing depth is limited, prioritize literature-supported breast cancer 
methylation loci (e.g., RARβ2, RASSF1A, Cyclin D2) for targeted comparison [1,3]. 
(5) Example tools: CpGassoc, DMRcate (R). 
 
8. Data Management and Statistical Analysis 
Data management 
De-identified datasets will be stored on secure hospital servers with access control. 
Linkage files will be kept separately by the PI. Paper records stored in locked 
cabinets. 
Statistical analysis 
(1) Group comparisons: For continuous features, t-test or Mann–Whitney U as 
appropriate; for categorical features, chi-square or Fisher’s exact tests. Two-sided 
tests; α=0.05. 

(2) Feature screening: Univariate logistic regression to estimate odds ratios and p-
values; variables with p<0.10 considered candidates. 
(3) Feature selection and modeling: Stepwise selection (AIC) or LASSO to finalize 
multivariable models. Core algorithms: logistic regression; exploratory: support 
vector machine, random forest. The reference standard is postoperative pathology 
(benign vs malignant). 
(4) Performance metrics: ROC curves, AUC with 95% CI; sensitivity, specificity, 
PPV, NPV; confusion matrices at prespecified thresholds. DeLong test to compare 
AUCs between combined models and single markers. 
(5) Internal validation: k-fold cross-validation (e.g., 5-fold) or bootstrap resampling to 
assess robustness and estimate variance. 
(6) Independent validation: Apply the locked model to an independent validation set 
(held-out or subsequently accrued cases); compute AUC, sensitivity, specificity, and 
95% CIs; recalibrate thresholds if prespecified. 
Sample size considerations 
This is an exploratory single-center study (n≈30) aimed at feasibility, feature 
discovery, and preliminary effect-size estimation rather than definitive hypothesis 
testing. Findings will inform power calculations for subsequent larger and/or 
multicenter studies. 
 
9. Ethical Considerations and Subject Protection 
(1) Ethics approval: The protocol and informed consent process have been reviewed 
and approved by the Ethics Committee of Hubei Cancer Hospital prior to initiation. 
Amendments will be submitted for ethics review. 
(2) Informed consent: Participants will receive clear verbal and written information, 
and consent will be obtained before any study procedures. 

(3) Risk/benefit: Nipple discharge collection is noninvasive with minor potential 
discomfort. Venipuncture may cause brief pain, bruising, rarely infection or vasovagal 
episodes. There is no direct clinical benefit; potential societal benefit lies in 
improving diagnostic strategies that may reduce unnecessary surgery in the future. 
(4) Privacy and confidentiality: Coded identifiers will be used for all samples and 
data. The linkage key is stored separately with restricted access. Publications will use 
aggregate, de-identified results only. 
(5) Costs and compensation: Participants will not be charged for study-specific 
procedures; no monetary compensation is provided. 
(6) Voluntary participation and withdrawal: Participation is voluntary; withdrawal is 
permitted at any time without impact on clinical care. Upon request, stored 
samples/data will be destroyed or no longer used. 
(7) Incidental findings: Because this is exploratory and not intended for clinical 
decision-making, individual molecular results will not be routinely returned. Any 
unexpected finding with clear, significant, and actionable clinical relevance will be 
considered by the study team and Ethics Committee before potential disclosure to the 
participant and treating physician. 
 
10. Quality Assurance and Monitoring 
The study will follow institutional SOPs for sample handling, data security, and 
laboratory procedures. Routine oversight will be performed by the PI and study 
coordinator. Given minimal risk and non-interventional design, a formal DSMB is not 
planned. 
 
11. Dissemination 
Results will be analyzed and disseminated via conference presentations and peer-

reviewed publications. Authorship and reporting will follow international guidelines 
and institutional policies. 
 
12. References 
[1] Pitarch M, Alcantara R, Comerma L, et al. An update on multimodal imaging 
strategies for nipple discharge: from detection to decision. Insights Imaging. 
2025;16(1):70. 
[2] Patuleia SIS, Suijkerbuijk KPM, van der Wall E, et al. Nipple Aspirate Fluid at a 
Glance. Cancers (Basel). 2021;14(1):[pagination not provided in source]. 
[3] Liu GY, Lu JS, Shen KW, et al. Fiberoptic ductoscopy combined with cytology 
testing in patients with spontaneous nipple discharge. Breast Cancer Res Treat. 
2008;108(2):271–277. 
[4] Janke F, Gasser M, Angeles AK, et al. Low-coverage whole genome sequencing of 
cell-free DNA to predict and track immunotherapy response in advanced non-small 
cell lung cancer. J Exp Clin Cancer Res. 2025;44(1):87. 
 
Administrative Information 
Version: v1.0 
Version Date: 05 September 2025 
Department: Breast Surgery, Hubei Cancer Hospital 
Sponsor/Institution: Hubei Cancer Hospital