Determination of temporal reproducibility and variability of cancer biomarkers in serum and EDTA plasma samples using a proximity extension assay.

BACKGROUND: Proximity extension assay (PEA) is a novel antibody-based proteomic technology. Sparse data have been published concerning the matrix effect of serum vs. ethylenediamine tetraacetic acid (EDTA) plasma and the reproducibility of results obtained using PEA technology. METHODS: We analyzed samples with the PEA-based 92-plex Olink® immuno-oncology (I-O) assay. To estimate the matrix effect, we analyzed paired serum and EDTA plasma samples from 12 patients with biliary tract cancer. To evaluate the reproducibility, we used data from 7 studies, where 6-8 serum samples from patients with pancreatic cancer were used as bridging samples on 3 versions of the panel over a 2.5-years period. RESULTS: For the study of serum vs. plasma, 80 proteins were evaluable. The mean serum to EDTA plasma ratio ranged from 0.41-3.01. For 36 proteins, the serum and plasma values were not comparable due to high variability of the ratio, poor correlation, or possible concentration effect. For the bridging samples, the mean intra-study inter-assay coefficient of variation (CV) ranged from 11.3% to 26.1%. The mean inter-study CV was 42.0% before normalization and 26.2% after normalization. Inter-study results were well correlated (r ≥ 0.93), especially for studies using the same version of the panel (r ≥ 0.99). CONCLUSION: For 44 of 92 proteins included in the Olink® I-O panel, the variation between results obtained using serum and EDTA plasma was constant and results were well correlated. Furthermore, samples could be stored for several years and used on different versions of the same PEA panel without it effecting results.

Development and validation of circulating protein signatures as diagnostic biomarkers for biliary tract cancer.

Background & Aims: Biliary tract cancer (BTC) is associated with a dismal prognosis, partly because it is typically diagnosed late, highlighting the need for diagnostic biomarkers. The purpose of this project was to identify and validate multiprotein signatures that could differentiate patients with BTC from non-cancer controls. Methods: In this study, we included treatment-naïve patients with BTC, healthy controls, and patients with benign conditions including benign biliary tract disease. Participants were divided into three non-overlapping cohorts: a case-control-based discovery cohort (BTC = 186, controls = 249); a case-control-based validation cohort (validation cohort 1: BTC = 113, controls = 241); and a cohort study-based validation cohort including participants (BTC = 8, controls = 132) referred for diagnostic work-up for suspected cancer (validation cohort 2). Immuno-Oncology (I-O)-related proteins were measured in serum and plasma using a proximity extension assay (Olink Proteomics). Lasso and Ridge regressions were used to generate protein signatures of I-O-related proteins and carbohydrate antigen 19-9 (CA19-9) in the discovery cohort. Results: Sixteen protein signatures, including 2 to 82 proteins, were generated. All signatures included CA19-9 and chemokine C-C motif ligand 20. Signatures discriminated between patients with BTC vs. controls, with AUCs ranging from 0.95 to 0.99 in the discovery cohort and 0.94 to 0.97 in validation cohort 1. In validation cohort 2, AUCs ranged from 0.84 to 0.94. Nine signatures achieved a specificity of 82% to 84% while keeping a sensitivity of 100% in validation cohort 2. All signatures performed better than CA19-9, and signatures including >15 proteins showed the best performance. Conclusion: The study demonstrated that it is possible to generate protein signatures that can successfully differentiate patients with BTC from non-cancer controls. Impact and implications: We attempted to find blood sample-based protein profiles that could differentiate patients with biliary tract cancer from those without cancer. Several profiles were found and tested in different groups of patients. The profiles were successful at identifying most patients with biliary tract cancer, pointing towards the utility of multiprotein signatures in this context.

Protein Signatures and Individual Circulating Proteins, including IL-6 and IL-15, Associated with Prognosis in Patients with Biliary Tract Cancer.

Biliary tract cancer (BTC) is a rare gastrointestinal cancer with a dismal prognosis. Biomarkers with clinical utility are needed. In this study, we investigated the association between survival and 89 immuno-oncology-related proteins, with the aim of identifying prognostic biomarkers for BTC. The study included patients with BTC (n = 394) treated at three Danish hospitals. Patients were divided into four cohorts: the first-line discovery cohort (n = 202), first-line validation cohort (n = 118), second-line cohort (n = 56), and surgery cohort (n = 41). Plasma protein levels were measured using a proximity extension assay (Olink Proteomics). Twenty-seven proteins were associated with overall survival (OS) in a multivariate analysis in the discovery cohort. In the first-line validation cohort, high levels of interleukin (IL)-6, IL-15, mucin 16, hepatocyte growth factor, programmed cell death ligand 1, and placental growth factor were significantly associated with poor OS in univariate Cox regression analyses. When adjusting for performance status, location, and stage, the association was significant only for IL-6 (hazard ratio (HR) = 1.25, 95% confidence interval (CI) 1.08-1.46) and IL-15 (HR = 2.23, 95% CI 1.48-3.35). Receiver operating characteristic analyses confirmed IL-6 and IL-15 as the strongest predictors of survival. Combining several proteins into signatures further improved the ability to distinguish between patients with short (<6 months) and long survival (>18 months). The study identified several circulating proteins as prognostic biomarkers in patients, with BTC, IL-6, and IL-15 being the most promising markers. Combining proteins in a prognostic signature improved prognostic performance, but future studies are needed to determine the optimal combination and thresholds.

Prognostic value of circulating proteins in patients undergoing surgery for pancreatic cancer.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer death. Less than 20% of patients are diagnosed with resectable disease. Identifying truly resectable disease is challenging because 20%-40% of the patients subjected to resection are found to have advanced disease during surgery. The aim of our study was to identify panels of circulating proteins that could be used to distinguish patients with unresectable PDAC from patients with resectable PDAC and to identify prognostic signatures for both groups. METHODS: We measured 92 circulating immuno-oncology-related proteins using the proximity extension assay from Olink Proteomics in 273 patients eligible for surgery for PDAC. Two bioinformaticians worked independently of one another on the same data. LASSO and Ridge regression were used in the statistical analyses. RESULTS: One protein index for determining resectability had an AUC value of 0.66. Several indices for prognosis had AUC values between 0.50 and 0.75 and were therefore not better than existing prognostic markers. DISCUSSION: Our study did not reveal any new high-performing protein panels that could be used to identify patients with inoperable PDAC before surgery. The panel of 92 proteins investigated has previously been found to be applicable for diagnostic use in patients with PDAC, but it does not seem to warrant further investigation regarding resectability in the subgroup of patients with PDAC referred to surgery.

Circulating Protein Biomarkers for Prognostic Use in Patients with Advanced Pancreatic Ductal Adenocarcinoma Undergoing Chemotherapy.

Patients with advanced pancreatic ductal adenocarcinoma (PDAC) have a dismal prognosis. We aimed to find a prognostic protein signature for overall survival (OS) in patients with advanced PDAC, and to explore whether early changes in circulating-protein levels could predict survival. We investigated 92 proteins using the Olink Immuno-Oncology panel in serum samples from 363 patients with advanced PDAC. Protein panels for several survival cut-offs were developed independently by two bioinformaticians using LASSO and Ridge regression models. Two panels of proteins discriminated patients with OS < 90 days from those with OS > 2 years. Index I (CSF-1, IL-6, PDCD1, TNFRSF12A, TRAIL, TWEAK, and CA19-9) had AUCs of 0.99 (95% CI: 0.98−1) (discovery cohort) and 0.89 (0.74−1) (replication cohort). For Index II (CXCL13, IL-6, PDCD1, and TNFRSF12A), the corresponding AUCs were 0.97 (0.93−1) and 0.82 (0.68−0.96). Four proteins (ANGPT2, IL-6, IL-10, and TNFRSF12A) were associated with survival across all treatment groups. Longitudinal samples revealed several changes, including four proteins that were also part of the prognostic signatures (CSF-1, CXCL13, IL-6, TNFRSF12A). This study identified two circulating-protein indices with the potential to identify patients with advanced PDAC with very short OS and with long OS.

Statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction.

Using data from patients with ST-elevation myocardial infarction (STEMI), we explored how machine learning methods can be used for analysing multiplex protein data obtained from proximity extension assays. Blood samples were obtained from 48 STEMI-patients at admission and after three months. A subset of patients also had blood samples obtained at four and 12 h after admission. Multiplex protein data were obtained using a proximity extension assay. A random forest model was used to assess the predictive power and importance of biomarkers to distinguish between the acute and the stable phase. The similarity of response profiles was investigated using K-means clustering. Out of 92 proteins, 26 proteins were found to significantly distinguish the acute and the stable phase following STEMI. The five proteins tissue factor pathway inhibitor, azurocidin, spondin-1, myeloperoxidase and myoglobin were found to be highly important for differentiating between the acute and the stable phase. Four of these proteins shared response profiles over the four time-points. Machine learning methods can be used to identify and assess novel predictive biomarkers as showcased in the present study population of patients with STEMI.

Circulating Protein Biomarkers for Use in Pancreatic Ductal Adenocarcinoma Identification.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal solid tumors. Most patients are diagnosed at an advanced stage where curative surgery is not an option. The aim of this study was to identify a panel of circulating proteins that could distinguish patients with PDAC from non-PDAC individuals. EXPERIMENTAL DESIGN: We investigated 92 proteins known to be involved in inflammation, development, and progression of PDAC using the Olink immuno-oncology panel in serum samples from 701 patients with PDAC (stage I-IV), 102 patients with nonmalignant pancreatic diseases, and 180 healthy blood donors. Patients were included prospectively between 2008 and 2018. Plasma carbohydrate antigen 19-9 (CA19-9) was measured in all samples. The protein panels with the best diagnostic performances were developed by two bioinformaticians working independently, using LASSO and Ridge regression models. RESULTS: Two panels of proteins (index I, containing 9 proteins + CA19-9, and index II, containing 23 proteins + CA19-9) were identified. Index I was able to discriminate patients with PDAC from all patients with non-PDAC, with a ROC AUC value of 0.92 [95% confidence interval (CI), 0.89-0.96] in the discovery cohort and 0.92 (95% CI, 0.87-0.97) in the replication cohort. For index II, the AUC value was 0.96 (95% CI, 0.95-0.98) in the discovery cohort and 0.93 (95% CI, 0.90-0.96) in the replication cohort. All nine serum proteins of index I were found in index II. CONCLUSIONS: This study identified two circulating protein indices with the potential to discriminate between individuals with and without PDAC.

The molecular profile of mucosal melanoma.

Herein, we wanted to explore the molecular landscape of mucosal melanoma from different sites and identify potential molecular targets for future therapy. Mucosal melanomas (N = 40) from different sites (conjunctiva, sinonasal cavity, rectum, and vagina) were investigated. Targeted next-generation sequencing along with Nanostring gene expression profiling was performed. Genetically, conjunctival melanoma was characterized by BRAF-V600E (30%) and NF1 mutations (17%). Mucosal melanomas at nonsun-exposed sites harbored alterations in NRAS, KIT, NF1, along with atypical BRAF mutations. When comparing the gene expression profile of conjunctival melanoma and nonsun-exposed mucosal melanoma, 41 genes were found to be significantly deregulated. Programmed death-ligand 1 (PD-L1) presented a significant sixfold upregulation in conjunctival melanoma compared to the other mucosal melanomas. While melanomas of the sinonasal cavity, vagina, and rectum are molecularly similar, conjunctival melanoma is characterized by a higher frequency of BRAF-V600E mutations and differential expression of several genes involved in the immune response.