Identification of a serum proteomic biomarker panel using diagnosis specific ensemble learning and symptoms for early pancreatic cancer detection

BACKGROUND The grim (<10% 5-year) survival rates for pancreatic ductal adenocarcinoma (PDAC) are attributed to its complex intrinsic biology and most often late-stage detection. The overlap of symptoms with benign gastrointestinal conditions in early stage further complicates timely detection. The suboptimal diagnostic performance of carbohydrate antigen (CA) 19-9 and elevation in benign hyperbilirubinaemia undermine its reliability, leaving a notable absence of accurate diagnostic biomarkers. Using a selected patient cohort with benign pancreatic and biliary tract conditions we aimed to develop a biomarker signature capable of distinguishing patients with non-specific yet concerning clinical presentations, from those with PDAC. METHODS 539 patient serum samples collected under the Accelerated Diagnosis of neuro Endocrine and Pancreatic TumourS (ADEPTS) study (benign disease controls and PDACs) and the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS, healthy controls) were screened using the Olink Oncology II panel, supplemented with five in-house markers. 16 specialized base-learner classifiers were stacked to select and enhance biomarker performances and robustness in blinded samples. Each base-learner was constructed through cross-validation and recursive feature elimination in a discovery set comprising approximately two thirds of the ADEPTS and UKCTOCS samples and contrasted specific diagnosis with PDAC. RESULTS The signature which was developed using diagnosis-specific ensemble learning demonstrated predictive capabilities outperforming CA19-9 and individual biomarkers in both discovery and validation sets. An AUC of 0.98 (95% CI 0.98 – 0.99) and sensitivity of 0.99 (95% CI 0.98 - 1) at 90% specificity was achieved with the ensemble method, which was significantly larger than the AUC of 0.79 (95% CI 0.66 - 0.91) and sensitivity 0.67 (95% CI 0.50 - 0.83), also at 90% specificity, for CA19- 9, in the discovery set (p=0.0016 and p=0.00050, respectively). During ensemble signature validation, an AUC of 0.95 (95% CI 0.91 – 0.99), sensitivity 0.86 (95% CI 0.68 - 1), was attained compared to an AUC of 0.80 (95% CI 0.66 – 0.93), sensitivity 0.65 (95% CI 0.48 – 0.56) at 90% specificity for CA19-9 alone (p=0.0082 and p=0.024, respectively). When validated only on the benign disease controls and PDACs collected from ADEPTS, the diagnostic-specific signature achieved an AUC of 0.96 (95% CI 0.92 – 0.99), sensitivity 0.82 (95% CI 0.64 – 0.95) at 90% specificity, which was still significantly higher than the performance for CA19-9 taken as a single predictor, AUC of 0.79 (95% CI 0.64-0.93) and sensitivity of 0.18 (95% CI 0.03 – 0.69) (p= 0.013 and p=0.0055, respectively). CONCLUSION Our ensemble modelling technique outperformed CA19-9, individual biomarkers and prevailing algorithms in distinguishing patients with non-specific but concerning symptoms from those with PDAC, with implications for improving its early detection in individuals at risk. ### Competing Interest Statement The authors declare the following competing interests: UM reports stock ownership in Abcodia UK between 2011 and 2021; UM has received grants from the Medical Research Council (MRC), Cancer Research UK, the National Institute for Health Research (NIHR), the India Alliance, NIHR Biomedical Research Centre at University College London Hospital, and The Eve Appeal; UM currently has research collaborations with iLOF, RNA Guardian and Micronoma, with funding paid to UCL; UM holds patent number EP10178345.4 for Breast Cancer Diagnostics; AG currently has research collaborations with Micronoma and iLoF, with the research funding awarded to UCL. No other potential conflicts of interest were disclosed by any of the authors. ### Funding Statement This research was funded by Cancer Research UK (grant C12077/A26223) and supported by the Pancreatic Cancer UK Early Diagnosis Award 2018, project "The Accelerated Diagnosis of neuroEndocrine and Pancreatic TumourS (ADEPTS)" (IRAS Number: 234637, NIHR Portfolio no. 7343), and by the National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre. UKCTOCS was core funded by the Medical Research Council, Cancer Research UK, and the Department of Health with additional support from the Eve Appeal, Special Trustees of Bart's and the London, and Special Trustees of University College London Hospitals. Prof Eithne Costello is supported by Cancer Research UK (C7690/A26881). Prof Usha Menon acknowledges MRC Core funding (MC\_UU\_00004/01). Prof Stephen Pereira is supported by Cancer Research UK Early Detection and Diagnosis Programme EDDPGM-May22\100002 (CANDETECT, Co-PI Prof Fiona Walter). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: We used serum samples from the Accelerated Diagnosis of neuro Endocrine and Pancreatic TumourS (ADEPTS) study (UCL/UCLH Research Ethics Committee reference 06/Q0512/106, IRAS Number 234637, NIHR portfolio no. 7343) study - an early detection study aimed at detecting pancreatic cancer in patients at an earlier stage. To further represent the healthy population we also used samples from 72 healthy control UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) samples which had been previously approved by the Joint UCL/UCLH Research Ethics Committee A (Ref. 05/Q0505/57). Written informed consent for the use of samples in the UKCTOCS trial and secondary ethically approved studies was obtained from donors and no data allowing identification of patients was provided. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data requestors will need to sign a data access agreement and in keeping with patient consent for secondary use obtain ethical approval for any new analyses.