
==== Front
Mol Cell Proteomics
Mol Cell Proteomics
Molecular & Cellular Proteomics : MCP
1535-9476
1535-9484
American Society for Biochemistry and Molecular Biology

S1535-9476(24)00114-2
10.1016/j.mcpro.2024.100824
100824
Research
Development of a Fit-For-Purpose Multi-Marker Panel for Early Diagnosis of Pancreatic Ductal Adenocarcinoma
Kim Hyeonji 1‡
Huh Sunghyun 1‡
Park Jungkap 2‡
Han Youngmin 3
Ahn Kyung-Geun 1
Noh Yiyoung 1
Lee Seong-Jae 1
Chu Hyosub 1
Kim Sung-Soo 4
Jung Hye-Sol 3
Yun Won-Gun 3
Cho Young Jae 3
Kwon Wooil 3
Jang Jin-Young jangjy4@snu.ac.kr
3∗
Kang Un-Beom unbeom.kang@bertis.com
1∗
1 Bertis R&D Division, Bertis Inc, Gyeonggi-do, Republic of Korea
2 Bertis Inc, Seoul, Republic of Korea
3 Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
4 Manufacturing and Technology Division, Bertis Inc, Gyeonggi-do, Republic of Korea
∗ For correspondence: Jin-Young Jang; Un-Beom Kang jangjy4@snu.ac.krunbeom.kang@bertis.com
‡ These authors contributed equally to this work.

05 8 2024
9 2024
05 8 2024
23 9 1008242 2 2024
28 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Pancreatic ductal adenocarcinoma (PDAC) suffers from a lack of an effective diagnostic method, which hampers improvement in patient survival. Carbohydrate antigen 19-9 (CA19–9) is the only FDA-approved blood biomarker for PDAC, yet its clinical utility is limited due to suboptimal performance. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a burgeoning technology in clinical proteomics for the discovery, verification, and validation of novel biomarkers. A plethora of protein biomarker candidates for PDAC have been identified using LC-MS, yet few has successfully transitioned into clinical practice. This translational standstill is owed partly to insufficient considerations of practical needs and perspectives of clinical implementation during biomarker development pipelines, such as demonstrating the analytical robustness of proposed biomarkers which is critical for transitioning from research-grade to clinical-grade assays. Moreover, the throughput and cost-effectiveness of proposed assays ought to be considered concomitantly from the early phases of the biomarker pipelines for enhancing widespread adoption in clinical settings. Here, we developed a fit-for-purpose multi-marker panel for PDAC diagnosis by consolidating analytically robust biomarkers as well as employing a relatively simple LC-MS protocol. In the discovery phase, we comprehensively surveyed putative PDAC biomarkers from both in-house data and prior studies. In the verification phase, we developed a multiple-reaction monitoring (MRM)-MS-based proteomic assay using surrogate peptides that passed stringent analytical validation tests. We adopted a high-throughput protocol including a short gradient (<10 min) and simple sample preparation (no depletion or enrichment steps). Additionally, we developed our assay using serum samples, which are usually the preferred biospecimen in clinical settings. We developed predictive models based on our final panel of 12 protein biomarkers combined with CA19-9, which showed improved diagnostic performance compared to using CA19-9 alone in discriminating PDAC from non-PDAC controls including healthy individuals and patients with benign pancreatic diseases. A large-scale clinical validation is underway to demonstrate the clinical validity of our novel panel.

Graphical Abstract

Highlights

• A novel clinical-grade biomarker panel is developed for PDAC diagnosis.

• Combining in-house and public datasets enables prioritization of protein biomarkers.

• Stringent analytical validation ensures the robustness of biomarker candidates.

• Simple LC-MS protocol is adopted to augment the assay’s translational potential.

• A combined panel of proteins and CA19-9 is superior to using CA19-9 only.

In Brief

The introduction of novel diagnostic methods for PDAC is partially hampered by the lack of successful translation of discovery findings into clinical assays. Clinical assay development requires sufficient consideration of practical needs and perspectives of clinical implementation, which are often neglected in conventional biomarker research. This study addresses important determinants associated with clinical translation, including verifying the analytical robustness of the measurements and ensuring throughput and cost-effectiveness of the assay. The final combined panel of proteins and CA19-9 shows enhanced diagnostic performance over using CA19-9 only.

Keywords

PDAC
early diagnosis
serum biomarker
multiple-reaction monitoring
analytical validation
Abbreviations

ACN acetonitrile

Ada adaboost

AJCC American Joint Committee on Cancer

CA19-9 carbohydrate antigen 19-9

CE collision energy

CV coefficient of variation

DEP differentially expressed protein

DIA data-independent acquisition

DMSO dimethyl sulfoxide

DTT dl-dithiothreitol

ECM extracellular matrix

ET extra-trees

FA formic acid

FDA Food and Drug administration

FDR false discovery rate

GB gradient boosting

GOBP gene ontology biological process

GSEA gene set enrichment analysis

HC healthy control

IAA iodoacetamide

IRB Institutional Review Board

KFDA Korea Food and Drug Administration

LC-MS liquid chromatography-mass spectrometry

LGBM lightgbm

LOWESS locally weighted scatterplot smoothing

MBR match-between-runs

MLP multi-layer perceptron

MRM multiple-reaction monitoring

PCA principal component analysis

PDAC pancreatic ductal adenocarcinoma

PEA Proximity Extension Assay

RF random forest

ROS reactive oxygen species

SIL stable isotope-labeled

TEAB triethylammonium bicarbonate buffer

TFA trifluoroacetic acid

XGB xgboost
==== Body
pmcPancreatic ductal adenocarcinoma (PDAC) poses a formidable challenge in oncology, characterized by a dismal 5-year survival rate of less than 10% (1). Conventional diagnostic modalities, including endoscopic ultrasound, computerized tomography, magnetic resonance imaging, and positron emission tomography, typically identify PDAC at advanced stages when tumors are unresectable, thereby significantly limiting effective treatment options (2). Therefore, early detection of PDAC stands as an imperative for enhancing patient survival, representing an outstanding unmet clinical need. Carbohydrate antigen 19-9 (CA19–9) is the only biomarker approved by the US Food and Drug Administration (FDA) for diagnosis and surveillance of PDAC (3). However, its efficacy remains suboptimal, with sensitivity and specificity of 79 to 81% and 82 to 90%, respectively, for PDAC diagnosis in symptomatic patients (4). Due to these limitations of the present screening methods, efforts have been underway to develop and implement novel diagnostic assays for PDAC (5, 6, 7). As of now, none of these endeavors have yet entered routine clinical practice, warranting further development of novel clinical assays for PDAC diagnosis.

Mass spectrometry (MS) has been the method of choice in discovery proteomics for several decades, owing to its unbiased capacity for deep profiling, unparalleled sensitivity and specificity, high reproducibility, and versatility as an analytical tool (8). More recently, MS is emerging as an enabling technology in clinical proteomics beyond biomarker discovery, complementing and even replacing in certain applications traditional immunoassays in biomarker verification and validation (9). Compared to immunoassays, targeted MS techniques such as multiple-reaction monitoring (MRM)-MS have superior multiplexing capability, selectivity, quantitative accuracy and precision, higher throughput, and are more economical to develop as clinical assays since they do not require antibodies specific to each measurand (10).

Despite these widely recognized advantages and potentials of MS-based proteomics in precision diagnostics, the number of clinical MS-based protein applications is still scarce (10). This is in stark contrast to the large number of publications reporting hundreds of potential protein biomarkers for multiple diseases in the proteomics literature (11). PDAC is no exception; despite the substantial volume of studies documenting protein biomarkers for PDAC, few have been incorporated into clinical-grade assays and undergone follow-up clinical validation, let alone transitioning into clinical practice (12). This wide gap between protein biomarker discovery and clinical application is increasingly being acknowledged and has recently been extensively discussed (11, 13, 14, 15, 16, 17). Ideally, a biomarker development pipeline initiates from early stages with sufficient considerations of the practical needs and perspectives of clinical implementation, including but not limited to demonstrating analytical robustness of the measurands and taking into account throughput and cost-effectiveness of assays developed. However, discovery proteomics studies are typically not planned and executed with concomitant considerations of these necessary steps for clinical translation and making a business case thereof. As a result, biomarker studies often do not go beyond initial discovery and accompanying publication, and even when attempts at transitioning discovery findings into clinical application are undertaken, they often fail to successfully qualify biomarker candidates in verification or validation phases. This has led to a large portion of biomarker studies as disparate and isolated data, bringing further development of these discovery findings to a standstill.

In light of these considerations, here we describe the initial two phases of our biomarker development pipeline, including biomarker discovery and verification, for the development of a fit-for-purpose multi-marker panel combining protein biomarkers and CA19-9 for PDAC diagnosis. To augment the translational potential of our pipeline, we systematically prioritized 35 promising biomarkers by combining findings from our in-house proteomics data and a comprehensive literature review of PDAC protein biomarkers in previous studies. In the verification phase, we performed stringent analytical validation experiments on all surrogate peptides, which resulted in 15 analytically robust peptides representing 15 proteins. Given the context-of-use of our proposed panel, a cost-effective and high-throughput initial screening test for PDAC, we developed a short gradient (<10 min) MRM-MS proteomic assay on serum samples with no depletion or enrichment steps. We verified our 15-protein assay in an orthogonal verification cohort consisting of PDAC patients, individuals with benign pancreatic diseases, and healthy controls (HCs). We then selected 12 protein biomarkers with the most consistent predictive performance and developed predictive models by combining the 12 proteins with CA19-9, which showed improved diagnostic performance over using CA19-9 alone.

Experimental Procedures

Declaration of Helsinki Principles

All serum specimens were collected with informed patient consent under a protocol approved by the Seoul National University Hospital Institutional Review Board (IRB) Ethics Committee (0901–010–267). This study was conducted according to the Declaration of Helsinki ethical principles.

Experimental Design and Statistical Rationale

Prioritization of promising protein biomarker candidates was first carried out in a discovery cohort of PDAC patients (n = 40) and healthy individuals (n = 40). The discovery samples were block-randomized prior to DIA-MS analysis by taking into account their histology, sex, and age. Final set of biomarker candidates was chosen by combining findings from prior studies with those from our in-house data. We then tested each biomarker for various analytical validation criteria to ensure these biomarkers are qualified for inclusion in a clinical assay. We then developed an MRM-MS-based clinical proteomic assay using the highly robust biomarkers on a verification cohort of PDAC patients (n = 154), healthy individuals (n = 152), and patients with benign pancreatic diseases (n = 50). The verification samples were block-randomized prior to MRM-MS analysis by taking into account their histology, sex, age, and receiving batch.

Study Population

For the discovery phase, serum samples from PDAC patients and healthy individuals were used. For the study group, we identified patients who met the following conditions: (1) those diagnosed with PDAC; (2) those who underwent primary surgical treatment between May 2015 and January 2020; and (3) those having donated their blood samples, obtained 1 day before surgery, for scientific purposes after providing written informed consent. Meanwhile, patients were excluded if they had any malignancy other than the respective conditions. For the control group, the samples from healthy individuals who visited the Seoul National University Hospital without cancer and inflammation were obtained. Healthy individuals who were newly diagnosed with cancer or with a prior history of cancer were excluded from the analysis.

For the verification phase, serum samples from patients with PDAC or benign pancreatic diseases, and healthy individuals were used. We first identified patients who met the following conditions: (1) those diagnosed with either PDAC or benign pancreatic diseases; (2) those who underwent primary surgical treatment between July 2013 and October 2020; and (3) those having donated their blood samples, obtained 1 day before surgery, for scientific purposes after providing written informed consent. Meanwhile, patients were excluded if they had any malignancy other than the respective conditions. For the HC group, the samples from healthy individuals who visited the Seoul National University Hospital were obtained.

We collected the patients’ baseline clinicopathologic characteristics, such as age at diagnosis, American Joint Committee on Cancer (AJCC) stage, and initial serum CA19-9 levels.

Reagents

The following reagents were used for sample preparation and MS experiments: Triethylammonium bicarbonate buffer (TEAB; Sigma); Acetonitrile (ACN; Macron, Avantor Performance Materials); DL-Dithiothreitol (DTT; Sigma); Iodoacetamide (IAA; Sigma); Trifluoroacetic acid (TFA; Pierce, Rockford, IL); Formic acid (FA; Sigma); and Dimethyl sulfoxide (DMSO; Sigma).

Serum Collection

Whole blood was collected by venipuncture with a 22G syringe and transferred to “vacutainer” serum separation tubes. They were centrifuged at 1500g for 10 min at 4 °C, and the supernatant layers were transferred to fresh tubes, and stored at − 80 °C until used. Only prior to mass spectrometry analysis, the frozen samples were thawed completely at 4 °C and vortexed lightly.

Serum Sample Preparation

A 5 μl aliquot of serum was diluted in 95 μl of 50 mM TEAB solution with 8 M urea and sonicated for 10 min. Protein was reduced (DTT, 10 mM, 56 °C, 30 min) and alkylated (IAA, 20 mM, room-temperature in dark, 30 min). Samples were then digested with 50 mM ammonium bicarbonate; trypsin (1:25 trypsin/protein) was added and incubated overnight at 37 °C. Then, 0.1% TFA was added to stop the enzymatic reaction. The concentration of the digested peptides was measured using NanoDrop and adjusted to 1 mg/ml. The resulting samples were vacuum-centrifuged to dry and stored at −80 °C until used.

Data-Independent Acquisition Analysis and Data Processing

Digested peptides were separated using a Dionex UltiMate 3000 RSLCnano system (Thermo Fisher Scientific). The tryptic peptides were reconstituted in 0.1% TFA and separated on an Acclaim Pepmap RSLC C18 column (150 mm × 150 μm i.d., 2 μm, 100 Å) equipped with a C18 Pepmap trap column (20 mm × 100 μm i.d., 5 μm, 100 Å; Thermo Scientific) over 120 min (1 μl/min) using a 5 to 40% ACN gradient in 0.1% FA and 5% DMSO at 50 °C. The LC was coupled to an Orbitrap Exploris 480 mass spectrometer with an EASY-SPRAY source (Thermo Fisher Scientific). Mass spectrometry runs were operated in DIA mode. For DIA experiments, full MS resolutions were set to 60,000 and the full MS AGC target was 500% with an IT of 20 m m/z range set to 350-1500. AGC target value for MS2 spectra was set to 300%. 70 windows of 9 Da were used with no overlap. Resolution was set to 15,000 and IT to 22 ms. NCE was set at 28.

The DIA-MS data were processed using DIA-NN (ver. 1.8.1) (18) with the following settings: library-free mode with a predicted library created using human reference database from UniProt (101,038 SwissProt and TrEMBL sequences; released on May 2022) plus 116 common contaminant proteins; up to two missed cleavages allowed; carbamidomethylation of cysteine set as a fixed modification and oxidation of methionine set as a variable modification; peptide length set from 7 to 40; precursor charge set from 1 to 4; m/z ranges set from 300 to 1200 for precursor and from 100 to 1700 for fragment ions; mass accuracies set to default value of 0.0 (i.e., DIA-NN determines mass tolerances automatically based on the first run in the experiment); and the match-between-runs (MBR) activated. Identification results were filtered at a false discovery rate (FDR) of 1% at precursor and protein levels (Lib.Q.Value < 0.01 and Lib.PG.Q.Value < 0.01, respectively, in the DIA-NN main output). For proteins identified using a single unique peptide, annotated spectra can be viewed using the search key “cjnveluz55” in the MS-Viewer (https://prospector.ucsf.edu/prospector/cgi-bin/msform.cgi?form=msviewer).

Batch Effect Correction

Initial assessment of our DIA-MS data revealed a noticeable variation in the MS run order rather than biological groups (Supplemental Fig. S2A). To correct for this batch effect, we adopted a continuous batch effect correction strategy previously proposed (19) to fit a non-linear curve for intensities of each MS feature (i.e., peptide) along the MS run order. In brief, we first aggregated the log2-transformed raw precursor intensities into peptides by taking their median value, and quantile normalized across all samples. Then, for each peptide, we applied a locally weighted scatterplot smoothing (LOWESS) to its normalized intensities to fit a non-linear curve along the MS run order and subtracted the fitted intensities from the initial unadjusted peptide intensities to obtain adjusted intensities. Then, we aggregated the adjusted peptide intensities into proteins by taking their median value, and quantile normalized across all samples. No noticeable batch effect by the MS run order was observed in the final adjusted data (Supplemental Fig. S2B).

Differential Expression Analysis

We defined differentially expressed proteins (DEPs) by applying an integrative statistical method previously reported (20). In brief, for each protein, we calculated test statistics using Students’ t test, the Wilcoxon Rank sum test, and a log2-median-ratio in each comparison. We then estimated empirical distributions of the test statistics and log2-median-ratios for the null hypothesis by randomly permutating all samples 1000 times. Using the estimated empirical distributions, for each protein, we computed adjusted p-values for the observed test statistics and log2-median-ratio and then calculated the overall p-value by combining these p-values using Stouffer’s method (21). Finally, we defined DEPs as the proteins that have overall p-values <0.1 and absolute log2-median-ratios greater than the mean of 10 and 90th percentile of the empirical distribution for log2-median-ratios in each comparison. Only the proteins that were expressed in more than 75% of total samples in both testing groups were used in downstream analyses.

Gene Set Enrichment Analysis

Gene set enrichment analysis (GSEA) was performed using ConsensusPathDB (22). Significantly enriched gene ontology biological processes (GOBPs) and biological pathways were identified as the ones with the enrichment p-value <0.05 and the number of molecules involved ≥10.

Literature Mining of PDAC Protein Biomarkers

We conducted a systematic literature mining of blood-based protein biomarkers for PDAC diagnosis. In brief, PubMed was searched for publications whose title, abstract, or full text contains the keywords “PDAC,” “diagnosis,” “biomarker,” “protein,” “plasma,” or “serum.” The search was limited to the studies published on or before July 2023. These studies were subsequently reviewed manually to retain only those specifically reporting on blood-based protein biomarkers for PDAC diagnosis. We included all studies that used either one of the following analytical techniques: MS-based profiling (DDA, DIA), targeted MS (MRM, parallel-reaction monitoring [PRM]), immunoassay (ELISA, western blotting), and Olink’s Proximity Extension Assay (PEA). Panels consisting of multi-omic compartments (e.g., protein + RNA) were also included if the molecular identity of the protein marker(s) was specified. The included studies were not limited to a particular tumor stage, and any study that contained a control group of healthy individuals and/or benign pancreatic diseases was considered. For studies that conducted verification or validation experiments of their proposed biomarkers identified in discovery findings, we only retained those proteins that passed the verification or validation experiments. For instance, if the differential expression pattern of a protein biomarker was identified in the discovery but not in validation phase, it was excluded from our final list.

Surrogate Peptide Selection

For the selection of surrogate peptides for MRM-MS, we generated in silico tryptic peptides for each protein candidate using the UniProtKB/SwissProt human protein database. Peptide sequences were required to meet the following criteria: unique in the human protein database, fully tryptic, no missed cleavage, length of 7 to 25 amino acids, not containing cysteine/methionine residues, and not corresponding to a protein’s N-terminal sequence. Additionally, we created a checklist to evaluate different properties of the peptide sequences for prioritization in the MRM assay; for each peptide, we considered the number of ragged tryptic sites and asparagine/glutamine/proline residues, and whether the sequence contains an N-glycosylation motif and a N-terminal glutamine. From this potential list of surrogate peptides, for each protein candidate, we prioritized at most three MRM-MS-compatible surrogate peptides by first querying the peptide sequences in the public MRM assay databases including the CPTAC assay (23) and SRMAtlas (24). If not included in these MRM assay databases, the peptide sequences were then queried in the Human Plasma PeptideAtlas database (25) to search for their previous identifications in the human plasma proteome. We selected the peptides that were included in these public databases as our top priority peptides. If the number of top priority peptides was greater than three, peptides having no ragged tryptic sites, N-glycosylation motif, N-terminal glutamine, and asparagine/glutamine residues were first selected, and among them, those containing at least one proline residue and with shorter length were then prioritized. For proteins whose top priority peptides that met all these criteria were less than three, we further included peptides by allowing up to one ragged tryptic site, N-glycosylation motif, or N-terminal glutamine, and up to two asparagine/glutamine residues.

Multiple-Reaction Monitoring Mass Spectrometry Analysis

Endogenous peptides of the 15 biomarker candidates in individual samples of the verification cohort and corresponding spiked stable isotope-labeled (SIL) peptides were analyzed by MRM-MS on a QTRAP 5500+ (Sciex). SIL peptides were obtained from ANYGEN, Korea, and BIOSTEM, Korea. Tryptic peptides were separated on a ZORBAX 300SB-C18 reverse phase column (0.5 × 150 mm, 3.5 μm; Agilent) over 9.5 min (20 μl/min) using a 3 to 35% ACN gradient in 0.1% FA. The collision energy (CE) value for each ionized peptide was determined by Skyline software (ver. 20.2) (26) providing value. MRM-MS data were analyzed by using AB Sciex Analyst software (ver. 1.7.2). Peak picking and determination of peak areas were first performed using DeepMRM (27) and then manually inspected.

Analytical Validation

The analytical validation experiments were designed to meet the requirements for validation practices of the Korea Food and Drug Administration (KFDA). The acceptance criteria for all analytical validation items were based on the KFDA guidance documents. These items covered such aspects as a calibration curve, selectivity, matrix effect, carryover, within- and between-run accuracy, within- and between-run precision, recovery, and freeze and thaw stability. Detailed descriptions of each item can be found in the Supplemental File. Only the surrogate peptides that passed all of the aforementioned criteria were used for Tier 2 assay development.

Statistical Analysis of the Verification Data

The Kruskal-Wallis test followed by Dunn’s post hoc test for multiple comparisons was used to estimate the statistical significance of all MRM-MS results in the verification cohort. The p-value <0.05 was considered significant.

Predictive Modeling

We developed multi-marker predictive models specifically designed to distinguish between PDAC samples and those from HC and/or benign pancreatic diseases. To maximize performance and robustness, we constructed a stacking ensemble model by integrating seven distinct machine-learning classification algorithms including extra-trees (ET), LightGBM (LGBM), random forest (RF), gradient boosting (GB), XGBoost (XGB), AdaBoost (Ada), and a multi-layer perceptron (MLP) consisting of a single hidden layer with 16 neurons. Base models were set with default hyperparameters. A logistic regression model serves as the meta-model, consolidating the predictions (i.e., predicted scores) of the base models in the ensemble.

The model takes as input the absolute abundance (ng/uL) of the 12 selected protein biomarkers and CA19-9 level (U/ml). Training and validation of the prediction models were carried out using five-fold cross-validation on the verification cohort in two separate ways: (1) using all samples encompassing 154 PDAC, 50 benign pancreatic diseases, and 152 HC samples, and (2) all samples but 50 benign pancreatic diseases. Model performance was compared against that of individual base models. Furthermore, to assess the diagnostic significance of the 12 biomarkers for PDAC, we included a logistic regression model using only the CA19–9 level in the comparison. All model development and experiments were conducted using Python 3.10 with scikit-learn v1.2, XGBoost v2.0, and LightGBM v3.3.5 libraries.

Results

Characterization of Study Population

For the development of a novel panel for PDAC diagnosis, we prepared two independent cohorts, each for the discovery and verification phase: (1) a discovery cohort consisting of 40 patients with PDAC and 40 age- and sex-matched HCs; and (2) a verification cohort consisting of 154 PDAC patients and an age- and sex-matched control group of 50 patients with benign pancreatic diseases and 152 HCs (Table 1). CA19-9 serum levels were measured for all individuals using ELISA. Samples from patients with PDAC were collected across all stages of PDAC (Supplemental Table S1).Table 1 Characteristics of the study population

Cohort	Discovery	Verification	
Histology	HCa	PDACb	HC	PDAC	Benignc	
No. cases	40	40	152	154	50	
	
Sex (Male/Female)	25/15	28/12	54/98	82/72	22/28	
Age range (median)	35–77 (60)	38–81 (64)	20–80 (53)	28–83 (66)	25–81 (58)	
CA19–9 level (U/ml)d range
(median)	2.0–49.1
(6.5)	2.0–1000.0
(102.8)	2.0–9.9
(7.3)	2.0–6298.0
(57.2)	2.0–1000.0
(7.23)	
AJCCe stage	-		-		-	
Stage I	-	9	-	27	-	
Stage II	-	6	-	91	-	
Stage III	-	15	-	25	-	
Stage IV	-	10	-	11	-	
a Healthy control.

b Pancreatic ductal adenocarcinoma.

c Benign pancreatic diseases including intraductal papillary mucinous neoplasm low-grade dysplasia (IPMN LGD), mucinous cystic neoplasm low-grade dysplasia (MCN LGD), neuroendocrine tumor grade 1 (NET Gr1), pancreatitis, serous cystic neoplasm (SCN), and solid pseudopapillary neoplasm (SPN).

d ELISA measurement values.

e American Joint Committee on Cancer.

Prioritization of Potential Diagnostic Biomarkers for PDAC

For prioritization of promising diagnostic biomarkers for PDAC, we employed two complementary strategies: (1) identification of DEPs using in-house proteomic profiling data, and (2) systematic review of potential protein biomarkers reported in the PDAC literature (Fig. 1A). For the in-house proteomics data, we performed a data-independent acquisition (DIA)-MS on neat serum samples of the discovery cohort. Database search resulted in the identification of 3814 peptides and 536 proteins. The number of identified peptides across samples varied in the range 522-3171 with an average of 2,810 and of proteins in the range 115 to 486 with an average of 424 (Supplemental Tables S2 and S3). We excluded one HC sample from all downstream analyses due to its significantly small number of identified molecules (Supplemental Fig. S1). Principal component analysis (PCA) showed a clear separation between the PDAC patients and HCs at the serum proteome level (Fig. 1B). GSEA showed dysregulation of biological processes in PDAC such as immune system, complement system, reactive oxygen species (ROS) metabolism, cell migration, and extracellular matrix (ECM) organization (Fig. 1C). We then performed differential expression analysis and identified 61 DEPs in the PDAC patients compared to HCs, including 23 up- and 38 down-regulated proteins (Fig. 1D and Supplemental Table S4).Fig. 1 Prioritization of potential diagnostic biomarkers for PDAC using a complementary approach combining in-house proteomic analysis and systematic literature mining. A, schematic representation of the potential biomarker selection process. B, principal component analysis (PCA) plot of the in-house serum proteomics data. Variance (in %) explained by each principal component is indicated. C, network representation of the enriched biological processes by gene set enrichment analysis (GSEA). Representative gene ontology biological processes (GOBPs) and biological pathways associated with PDAC are indicated. Node color, enrichment p-value; Node size, the number of proteins involved in each GOBP or pathway; Edge width, the number of overlapping proteins between nodes. D, heatmap showing the expression levels of differentially expressed proteins (DEPs) in PDAC vs HC. The numbers of up- and down-regulated proteins are indicated. E, summary of the biomarker candidates from the in-house proteomics analysis and literature mining. The numbers and gene symbols of the biomarker candidates that are unique to the in-house data or prior studies, or common between them are indicated.

To incorporate promising biomarkers from prior studies to harness the evolving knowledge in the field, we simultaneously conducted a systematic literature mining of PDAC protein biomarkers. We collected 120 potential biomarkers reported in one or more studies, among which 25 were identified as DEPs in our data (Fig. 1E and Supplemental Table S5). As an initial strategy to triage potential biomarkers, we first selected 26 promising biomarkers satisfying all the following three prioritization criteria: (1) having two or more previous reports as PDAC biomarkers or previously verified using a targeted quantification technique (either targeted MS or immunoassay), (2) defined as DEPs in our data, and (3) involved in one or more PDAC-associated pathways (Supplemental Table S6). Then, we went on to include more potential candidates by the following process: including (1) proteins that overlap in any two of the three aforementioned prioritization criteria, (2) not identified as DEPs but previously verified using targeted approaches, and (3) DEPs whose absolute fold-changes are the largest in our in-house data. This resulted in a total 66 candidates, and from this list, we narrowed down to fewer candidates by prioritizing proteins whose blood concentrations are higher than 107 pg/L as reported in the Human Plasma Atlas, and by manually inspecting their expression patterns in prior studies and/or our in-house data to qualitatively evaluate their effect size. Finally, 35 protein candidates were prioritized.

Analytical Validation of Surrogate Peptides

To select biomarker candidates with highly robust analytical characteristics, we conducted analytical validation experiments on the 35 protein candidates (Fig. 2A). The experiments for analytical validation were carried out using the same sample preparation protocols and MRM-MS methods intended to be used in the verification phase and up to clinical settings: namely, undepleted serum as blood biospecimen, and microflow LC (gradient <10 min) coupled to a widely used triple quadrupole mass spectrometer (Sciex QTRAP 5500+). For each protein, we selected at most three surrogate peptides and first tested their endogenous levels in pooled serum samples. Among them, 68 peptides representing 32 proteins were endogenously detected. Then we tested these 68 peptides for an exhaustive list of analytical validation criteria including linearity, selectivity, matrix effect, carryover, recovery, within- and between-run accuracy, within- and between-run precision, and stability (Supplemental Fig. S3 and Supplemental File). The following thresholds were set for the analytical validation criteria to evaluate the robustness of each surrogate peptide: for linearity, R2 > 0.99 and accuracy <20%; for selectivity, interference estimate <20%; for matrix effect, matrix factor coefficient of variation (CV) <15% in both low and high concentration samples; for carryover, peak area ratio of blank and LLOQ samples <20%; for recovery, CV <15% for all low, medium, and high concentration quality control (QC) samples; for within- and between-run accuracy & precision, accuracy for LLOQ QC sample and low, medium, and high concentration QC samples <20% and <15%, respectively; and for stability, accuracy <15% for all stability estimates. We applied stringent inclusion criteria such that only the peptides that passed cutoff values for all analytical validation criteria were retained.Fig. 2 Analytical validation of the surrogate peptides.A, workflow for selecting analytically robust peptides. B, example of a surrogate peptide ALEQALEK (representing alanyl aminopeptidase, membrane; ANPEP) that passed analytical validation. Cutoff values for each analytical validation experiment are highlighted in red. Individual values of each data point are indicated. CV, coefficient of variation. LLOQ, lower limit of quantification.

As a result, 19 robust peptides representing 15 proteins survived analytical validation. Detailed analytical validation results for all evaluated peptides are shown in Supplemental Table S7. An example of a surrogate peptide ALEQALEK (representing alanyl aminopeptidase, membrane; ANPEP) that passed analytical validation is shown in Figure 2B. The peptide had high linearity (R2 = 0.9999) and accuracy (0.43 ∼ 6.14%) in all dilution concentrations, low interference (7.55 ∼ 18.36%) in all replicates, low matrix factor in both low (CV = 8.22%) and high (CV = 2.32%) concentration QC samples, low peak area ratio (5.94%) of blank and LLOQ QC samples, stable recovery for low (CV = 2.64%), medium (CV = 4.61%), and high (CV = 3.94%) concentration QC samples, good accuracy for within- (0.72 ∼ 1.94%) and between-run (0.41 ∼ 14.41%) accuracy as well as within- (3.07 ∼ 10.92%) and between-run (7.08 ∼ 10.24%) precision, and good accuracy for freeze & thaw (0.72∼ 11.22% and 3.23 ∼ 13.75% for thaw 1 and 2 stability, respectively), short & long (4.25 ∼ 14.55% and 9.32 ∼ 14.27% for short and long term, respectively), and processed sample stability (3.39 ∼ 14.85% and 1.52 ∼ 14.22% for Day 3 and 5, respectively) for all replicates. All other peptides with one or more unsatisfied criteria were removed (see Supplemental Fig. S4 for an example of a failed surrogate peptide). Finally, for proteins with multiple robust peptides, we selected a single best surrogate peptide with the strongest signal in MRM-MS, resulting in 15 surrogate peptides representing 15 proteins. Collectively, using stringent guidelines for analytical validation, we selected 15 highly robust biomarker candidates to further develop them into an accurate and reliable proteomic assay.

Verification of Potential Diagnostic Biomarkers for PDAC

Using these 15 candidates, we then developed an MRM-MS-based proteomic assay on the verification cohort (Fig. 3A). We employed a method involving a short gradient (<10 min) on a commonly used triple quadrupole mass spectrometer (Sciex QTRAP 5500+), and a relatively simple sample preparation procedure without depletion of high-abundant proteins or enrichment steps, rendering our proteomic assay suitable for high-throughput screening and broad clinical implementation. As in the discovery phase, we performed all verification experiments on neat serum samples which we also intend to use in later phases of our development pipeline including clinical validation and finally clinical practice. Using SIL peptides of the 15 candidates, we performed MRM-MS experiments on the verification cohort consisting of 154 PDAC patients, 50 patients with benign pancreatic diseases, and 152 HCs (Supplemental Table S8). To retain only the biomarkers with the most consistent and high predictive performance in distinguishing PDAC from non-PDAC controls, we assessed their feature importance in an LGBM model across a five-fold cross-validation. We ranked the feature importance of each marker and selected the five markers with the lowest ranks in each fold. Three markers appearing most frequently in the bottom five ranks in three or more folds were subsequently excluded. As a result, 12 proteins were chosen as the final biomarkers to be included in our proteomic assay: ANPEP, APOA4, C9, HGFAC, IGFBP2, ITIH3, LRG1, PFN1, PIGR, PON3, SERPINA3, and VWF (shown in gene names, Supplemental Table S9). These proteins showed expected expression patterns in PDAC when compared to controls (Fig. 3B), considering patterns found in our discovery experiments as well as prior studies. All proteins except HGFAC showed statistically significant (p < 0.05) differential expressions in at least one comparison group of interest (i.e., early-stage PDAC versus HC, early-stage PDAC versus benign, advanced-stage PDAC versus HC, or advanced-stage PDAC versus benign). Depending on the comparison groups, some proteins did not pass the significance threshold; however, most of them showed a clear trend toward either up- or downregulation as expected. We did not rule out these ‘marginally significant’ proteins as potential biomarkers since they can contribute to improved diagnostic value when used in combination with other proteins. Moreover, all proteins except HGFAC were significantly altered already in early stages (I/II) compared to HC or benign diseases, indicating they can be useful in early diagnosis of PDAC.Fig. 3 Verification of the clinical proteomic assay for PDAC diagnosis using targeted MS approaches. A, schematic workflow for the development of the clinical panel. B, Boxplots showing the expressions (in absolute abundance, ng/uL) of the 12 protein biomarkers (shown in gene names) in the verification cohort (∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.005, Kruskal-Wallis test with Dunn’s post hoc analysis).

Diagnostic Performance of the Novel Biomarker Panel for PDAC Diagnosis

As the last step of the verification phase, we developed predictive models for distinguishing PDAC from HC and/or benign pancreatic diseases. Model training and evaluation were performed through stratified five-fold cross-validation, preserving the proportional representation of each class (i.e., PDAC, benign, or HC) within the folds. Model performance was assessed using sensitivity, specificity, and ROC AUC score metrics. These metrics were computed based on the entire test results collected from each fold.

Predictive models incorporating the 12 protein biomarkers in conjunction with CA19-9 level exhibited superior ROC AUC (0.870–0.924 and 0.869–0.901 for HC only and HC + Benign as control, respectively) compared to the models using only CA19-9 (0.826 and 0.818 for HC only and HC + Benign as control, respectively), as shown in Supplemental Table S10. Specifically, within high sensitivity areas (e.g., >0.8), these models significantly improved specificity compared to the CA19-9-only models. The devised stacking ensemble model demonstrated the highest ROC AUC score (0.924, PDAC vs HC only), surpassing other models in both sensitivity and specificity. For instance, at a sensitivity of 0.85, while other models showed specificities between 0.368 and 0.828, the ensemble model achieved the highest specificity of 0.849.

Figure 4 and Table 2 present the comparative model performances of the ensemble models and CA19-9-only models for discriminating all-stage PDAC or early-stage PDAC (i.e., stage I/II) from respective controls. The ROC AUC for the ensemble model in discriminating early-stage PDAC from HC only (0.920) was comparable to that in all-stage PDAC vs HC only (0.924). Within high sensitivity areas, the ensemble model in discriminating early-stage PDAC vs HC only showed specificities (0.566–0.875) comparable to those in all-stage PDAC vs HC only (0.539–0.888) as shown in Supplemental Table S9. Similarly, the ROC AUC for the ensemble model in discriminating early-stage PDAC from HC + Benign only (0.889) was comparable to that in all-stage PDAC vs HC + Benign (0.898). Within high sensitivity areas, the ensemble model in discriminating early-stage PDAC vs HC + Benign showed specificities (0.361–0.797) comparable to those in all-stage PDAC vs HC + Benign (0.460–0.817) as shown in Supplemental Table S9. Collectively, these results demonstrate that our combined panel incorporating the 12 protein biomarkers and CA19-9 shows superior diagnostic performance compared to using CA19-9 only and can be effective in detecting early-stage PDAC.Fig. 4 ROC curves representing the performances of the stacking ensemble models based on the combined panel and models using CA19-9 only. ROC curves for models discriminating PDAC patients from HCs (A) and from HCs and patients with benign pancreatic diseases (B). Area under the curve (AUC) values are indicated.

Table 2 ROC AUC values of the stacking ensemble models based on the combined panel of the 12 protein biomarkers and CA19-9 and models using CA19-9 only

Comparison group	ROC AUC	
CA19–9 only	Combined panel	
PDAC (all-stage) vs HC	0.826	0.924	
PDAC (I/II) vs HC	0.824	0.920	
PDAC (all-stage) vs HC + Benign	0.818	0.898	
PDAC (I/II) vs HC + Benign	0.819	0.889	

Discussion

In this study, we present a biomarker pipeline, from discovery to verification, for the development of a clinical panel for PDAC diagnosis, addressing some of the important limitations and challenges associated with the translational gap in clinical proteomics. Emphasis on discovery without sufficient consideration for clinical application is often attributed as one of the culprits of translational standstill. The superior performance of newly discovered biomarkers to that of current standards does not automatically result in successful clinical implementation (13). Robust qualification of analytical characteristics of the measurands and considerations of the practical usefulness of the assay in clinical settings including high throughput and cost-effectiveness are, among others, indispensable components for the successful development of a clinical assay. Yet, these important aspects are often overlooked in conventional biomarker research, as many biomarker studies do not go beyond the discovery phase, systematically validate the analytical performances of their biomarker candidates, or take into consideration the throughput and cost of their assays from the beginning. To bridge this translational gap in clinical proteomics in general and PDAC diagnosis in particular, we developed a multi-marker panel combining an MRM-MS-based proteomic assay with CA19-9 ELISA measurement through a systematic and robust development process, successfully transitioning biomarker candidates from discovery to verification.

In the discovery phase, we strategically identified an initial panel of 35 putative biomarkers by combining findings from in-house proteomics data and prior PDAC biomarker studies, ensuring a comprehensive selection process. The verification phase involved rigorous analytical validation of surrogate peptides, which is crucial for establishing the quantitative accuracy and reliability of the selected biomarkers. The eight criteria from linearity to stability described in our study provide an analytical validation of a Tier 2 assay. Of note, for analytical validation, we used the same simple, cost-effective, high-throughput protocols for sample preparation (i.e., undepleted serum samples) and MRM-MS analysis (i.e., short gradient) which we employed in the verification phase and intend to use in validation as well as clinical practice in the future. We decided to retain only those peptides that passed all the aforementioned analytical validation criteria, which may have resulted in the removal of a large portion of initial biomarker candidates (i.e., 20 out of 35 proteins removed), particularly considering the high-throughput settings in these experiments. On the other hand, these strict criteria for analytical validation can contribute to the successful transition of our assay further down the biomarker development pipeline including clinical validation and eventually clinical practice. Moreover, our assay was designed with its intended use in mind, considering the need for a cost-effective and high-throughput initial screening test for PDAC. The use of a short gradient (<10 min), no depletion or enrichment steps, and serum as the biospecimen aligns with common practical challenges in clinical settings including analysis throughput and cost, potentially facilitating ease of implementation. As noted before (12), biomarker panels for PDAC show the best diagnostic performance when novel biomarkers are combined with CA19-9. Following this line of advice, we integrated the 12 protein biomarkers with CA19-9 to form our final panel, which showed improved diagnostic performance compared to using CA19-9 alone. All 12 protein biomarkers in our panel have previously been characterized for their expression patterns in PDAC on various biospecimens including biofluids and tumor tissues, as well as their functional associations with initiation, progression, or tumor microenvironment changes in PDAC or other cancers (Supplemental Table S11).

The biomarker pipeline presented in the current study was in large part inspired by Mastocheck, a proteomic diagnostic assay for breast cancer previously commercialized by our group (28, 29). The assay was developed based on short-gradient (∼10 min) MRM-MS using neat serum samples, which contributed to its successful implementation as a high-throughput, cost-effective clinical assay that has penetrated >400 health check-up centers in South Korea (internal data). We expect that similar design elements utilized for the development of our novel PDAC biomarker panel can enhance the feasibility of its widespread adoption and integration into existing diagnostic workflows.

In conclusion, our study contributes to addressing the translational challenges in PDAC biomarker research by presenting a robust biomarker pipeline that takes into consideration some of the critical practical necessities regarding the implementation of clinical assays. We believe our novel multi-marker panel represents a significant step forward in the pursuit of an effective and practical diagnostic tool for PDAC. We are currently engaged in a large-scale clinical validation study to demonstrate the clinical validity of our panel.

Data Availability

DIA-MS data have been deposited to the ProteomeXchange Consortium via the PRIDE (30) partner repository with the dataset identifier PXD048034. MRM-MS data can be accessed on the Panorama Public website using the following link: https://panoramaweb.org/lVOW5s.url.

Supplemental data

This article contains supplemental data.

Conflict of interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

H. K., S. H., and J. P. reported being members of Bertis Inc. S. S. K. and U. B. K. reported being members of Bertis Inc. and holding stock options of the Bertis Inc. J. Y. J. reported receiving honoraria for advisory board participation from Bertis Inc. No other disclosures were reported.

Supplementary Data

Supplemental File

Supplemental Table

Acknowledgments

This project was supported by the Seoul R&BD program at Bertis Inc (BT220219 ). We are grateful to Seoul National University Hospital for providing human blood samples.

Author contributions

H. K., S. H., and U. B. K. conceptualization; H. K. and J. P. predictive modeling; K. G. A., Y. N., S. J. L., and S. S. K. proteomic data generation; Y. H., H. S. J., W. G. Y., Y. J. C., and W. K. serum sample collection and preparation; H. K., S. H., and J. P. writing–original draft; H. K., S. H., U. B. K., and J. Y. J. writing–review & editing.
==== Refs
References

1 Bengtsson A. Andersson R. Ansari D. The actual 5-year survivors of pancreatic ductal adenocarcinoma based on real-world data Sci. Rep. 10 2020 16425
2 Ghaneh P. Kleeff J. Halloran C.M. Raraty M. Jackson R. Melling J. The impact of positive resection margins on survival and recurrence following resection and adjuvant chemotherapy for pancreatic ductal adenocarcinoma Ann. Surg. 269 2019 520 529 29068800
3 Ballehaninna U.K. Chamberlain R.S. Biomarkers for pancreatic cancer: promising new markers and options beyond CA 19-9 Tumour Biol. 34 2013 3279 3292 23949878
4 Ballehaninna U.K. Chamberlain R.S. The clinical utility of serum CA 19-9 in the diagnosis, prognosis and management of pancreatic adenocarcinoma: an evidence based appraisal J. Gastrointest. Oncol. 3 2012 105 119 22811878
5 Brand R.E. Persson J. Bratlie S.O. Chung D.C. Katona B.W. Carrato A. Detection of early-stage pancreatic ductal adenocarcinoma from blood samples: results of a multiplex biomarker signature validation study Clin. Transl. Gastroenterol. 13 2022 e00468
6 Klein E.A. Richards D. Cohn A. Tummala M. Lapham R. Cosgrove D. Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set Ann. Oncol. 32 2021 1167 1177 34176681
7 Mellby L.D. Nyberg A.P. Johansen J.S. Wingren C. Nordestgaard B.G. Bojesen S.E. Serum biomarker signature-based liquid biopsy for diagnosis of early-stage pancreatic cancer J. Clin. Oncol. 36 2018 2887 2894 30106639
8 Aebersold R. Mann M. Mass-spectrometric exploration of proteome structure and function Nature 537 2016 347 355 27629641
9 Parker C.E. Borchers C.H. Mass spectrometry based biomarker discovery, verification, and validation--quality assurance and control of protein biomarker assays Mol. Oncol. 8 2014 840 858 24713096
10 Smit N.P.M. Ruhaak L.R. Romijn F.P.H.T.M. Pieterse M.M. van der Burgt Y.E.M. Cobbaert C.M. The time has come for quantitative protein mass spectrometry tests that target unmet clinical needs J. Am. Soc. Mass Spectrom. 32 2021 636 647 33522792
11 Mundt F. Albrechtsen N.J.W. Mann S.P. Treit P. Ghodgaonkar-Steger M. O’Flaherty M. Foresight in clinical proteomics: current status, ethical considerations, and future perspectives Open Res. Europe 3 2023 59
12 Kane L.E. Mellotte G.S. Mylod E. O’Brien R.M. O’Connell F. Buckley C.E. Diagnostic accuracy of blood-based biomarkers for pancreatic cancer: a systematic review and meta-analysis Cancer Res. Commun. 2 2022 1229 1243 36969742
13 Mischak H. Ioannidis J.P.A. Argiles A. Attwood T.K. Bongcam-Rudloff E. Broenstrup M. Implementation of proteomic biomarkers: making it work Eur. J. Clin. Invest. 42 2012 1027 1036 22519700
14 Füzéry A.K. Levin J. Chan M.M. Chan D.W. Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges Clin. Proteomics 10 2013 13 24088261
15 Nkuipou-Kenfack E. Zürbig P. Mischak H. The long path towards implementation of clinical proteomics: exemplified based on CKD273 Proteomics Clin. Appl. 11 2017 10.1002/prca.201600104
16 Vlahou A. Back to the future in bladder cancer research Expert Rev. Proteomics 8 2011 295 297 21679109
17 Boja E.S. Fehniger T.E. Baker M.S. Marko-Varga G. Rodriguez H. Analytical validation considerations of multiplex mass-spectrometry-based proteomic platforms for measuring protein biomarkers J. Proteome Res. 13 2014 5325 5332 25171765
18 Demichev V. Messner C.B. Vernardis S.I. Lilley K.S. Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput Nat. Methods 17 2020 41 44 31768060
19 Čuklina J. Lee C.H. Williams E.G. Sajic T. Collins B.C. Rodríguez Martínez M. Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial Mol. Syst. Biol. 17 2021 e10240
20 Chae S. Ahn B.Y. Byun K. Cho Y.M. Yu M.-H. Lee B. A systems approach for decoding mitochondrial retrograde signaling pathways Sci. Signal. 6 2013 rs4
21 Hwang D. Rust A.G. Ramsey S. Smith J.J. Leslie D.M. Weston A.D. A data integration methodology for systems biology Proc. Natl. Acad. Sci. U. S. A. 102 2005 17296 17301 16301537
22 Kamburov A. Pentchev K. Galicka H. Wierling C. Lehrach H. Herwig R. ConsensusPathDB: toward a more complete picture of cell biology Nucleic Acids Res. 39 2011 D712 D717 21071422
23 Whiteaker J.R. Halusa G.N. Hoofnagle A.N. Sharma V. MacLean B. Yan P. CPTAC Assay Portal: a repository of targeted proteomic assays Nat. Methods 11 2014 703 704 24972168
24 Kusebauch U. Campbell D.S. Deutsch E.W. Chu C.S. Spicer D.A. Brusniak M.-Y. Human SRMAtlas: a resource of targeted assays to quantify the complete human proteome Cell 166 2016 766 778 27453469
25 Deutsch E.W. Eng J.K. Zhang H. King N.L. Nesvizhskii A.I. Lin B. Human plasma PeptideAtlas Proteomics 5 2005 3497 3500 16052627
26 MacLean B. Tomazela D.M. Shulman N. Chambers M. Finney G.L. Frewen B. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments Bioinformatics 26 2010 966 968 20147306
27 Park J. Wilkins C. Avtonomov D. Hong J. Back S. Kim H. Targeted proteomics data interpretation with DeepMRM Cell Rep. Methods 3 2023 100521
28 Kim Y. Kang U.-B. Kim S. Lee H.-B. Moon H.-G. Han W. A validation study of a multiple reaction monitoring-based proteomic assay to diagnose breast cancer J. Breast Cancer 22 2019 579 586 31897331
29 Lee H.-B. Kang U.-B. Moon H.-G. Lee J. Lee K.-M. Yi M. Development and validation of a novel plasma protein signature for breast cancer diagnosis by using multiple reaction monitoring-based mass spectrometry Anticancer Res. 35 2015 6271 6279 26504062
30 Perez-Riverol Y. Bai J. Bandla C. García-Seisdedos D. Hewapathirana S. Kamatchinathan S. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences Nucleic Acids Res. 50 2022 D543 D552 34723319
