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To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

Following publication of the original article [1], the authors reported the name of R.J. Baatenburg de Jong was incorrectly tagged in the HTML version of the article.

Background: Worldwide, over 500,000 people are diagnosed with head and neck cancer each year, a disease with major impact on life expectancy and quality of life. The purpose of the Netherlands Quality of life and Biomedical Cohort study (NET-QUBIC) is to advance interdisciplinary research that aims to optimize diagnosis, treatment, and supportive care for head and neck cancer patients and their informal caregivers. Methods: Using an extensive assessment protocol (electronic clinical record form, patient reported outcome measures and fieldwork (interviews and physical tests)), clinical data and data on quality of life, demographic and personal factors, psychosocial (depression, anxiety, fatigue, pain, sleep, mental adjustment to cancer, posttraumatic stress), physical (speech, swallowing, oral function, malnutrition, physical fitness, neurocognitive function, sexual function), lifestyle (physical activity, nutrition, smoking, alcohol, drugs), and social factors (social function, social support, work, health care use, and costs) are collected and stored in the data warehouse. A longitudinal biobank is built with tumor tissue, blood and blood components, saliva samples, and oral rinses. An infrastructure for fieldwork and laboratory protocols is estab

Background: Oropharyngeal squamous cell carcinomas (OPSCCs) are traditionally caused by smoking and excessive alcohol consumption. However, in the last decades high-risk human papillomavirus (HPV) infections play an increasingly important role in tumorigenesis. HPV-driven OPSCCs are known to have a more favorable prognosis, which has led to important and marked changes in the recently released TNM-8. In this 8th edition, OPSCCs are divided based on p16 immunostaining, with p16 overexpression as surrogate marker for the presence of HPV. The aims of this study are to evaluate TNM-8 on a Dutch consecutive cohort of patients with p16-positive OPSCC and to determine the relevance of additional HPV DNA testing. Patients and methods: All OPSCC patients without distant metastases at diagnosis and treated with curative intent at VU University Medical Center (2000-2015) and Erasmus Medical Center (2000-2006) were included (N=1204). HPV status was determined by p16 immunostaining followed by HPV DNA PCR on the p16-immunopositive cases. We compared TNM-7 and TNM-8 using the Harrell's C index. Results: In total, 388 of 1204 (32.2%) patients were p16-immunopositive. In these patients, TNM-8 had a markedly better predictive prognostic power than TNM-7 (Harrell's C index 0.63 versus 0.53). Of the 388 p16-positive OPSCCs, 48 tumors (12.4%) were HPV DNA-negative. This subgroup had distinct demographic, clinical and morphologic characteristics and showed a significantly worse five-year overall survival compared with the HPV DNA-positive tumors (P < 0.001). Conclusions: TNM-8 has a better predictive prognostic power than TNM-7 in patients with p16-positive OPSCC. However, within p16-positive OPSCCs, there is an HPV DNA-negative subgroup with distinct features and a worse overall survival, indicating the importance to perform additional HPV DNA testing when predicting prognosis and particularly for selecting patients for de-intensified treatment regimens.

Background: Prediction in high dimensional settings is difficult due to the large number of variables relative to the sample size. We demonstrate how auxiliary 'co-data' can be used to improve the performance of a Random Forest in such a setting. Results: Co-data are incorporated in the Random Forest by replacing the uniform sampling probabilities that are used to draw candidate variables by co-data moderated sampling probabilities. Co-data here are defined as any type information that is available on the variables of the primary data, but does not use its response labels. These moderated sampling probabilities are, inspired by empirical Bayes, learned from the data at hand. We demonstrate the co-data moderated Random Forest (CoRF) with two examples. In the first example we aim to predict the presence of a lymph node metastasis with gene expression data. We demonstrate how a set of external p-values, a gene signature, and the correlation between gene expression and DNA copy number can improve the predictive performance. In the second example we demonstrate how the prediction of cervical (pre-)cancer with methylation data can be improved by including the location of the probe relative to the known CpG islands, the number of CpG sites targeted by a probe, and a set of p-values from a related study. Conclusion: The proposed method is able to utilize auxiliary co-data to improve the performance of a Random Forest.

Purpose: Current assessment of lymph node metastasis in patients with head and neck squamous cell carcinoma is not accurate enough to prevent overtreatment. The aim of this study was validation of a gene expression signature for distinguishing metastasizing (N+) from nonmetastasizing (N0) squamous cell carcinoma of the oral cavity (OSCC) and oropharynx (OPSCC) in a large multicenter cohort, using a diagnostic DNA microarray in a Clinical Laboratory Improvement Amendments/International Organization for Standardization-approved laboratory. Methods: A multigene signature, previously reported as predictive for the presence of lymph node metastases in OSCC and OPSCC, was first re-evaluated and trained on 94 samples using generic, whole-genome, DNA microarrays. Signature genes were then transferred to a dedicated diagnostic microarray using the same technology platform. Additional samples (n = 222) were collected from all head and neck oncologic centers in the Netherlands and analyzed with the diagnostic microarray. Human papillomavirus status was determined by real-time quantitative polymerase chain reaction. Results: The negative predictive value (NPV) of the diagnostic signature on the entire validation cohort (n = 222) was 72%. The signature performed well on the most relevant subset of early-stage (cT1-T2N0) OSCC (n = 101), with an NPV of 89%. Conclusion: Combining current clinical assessment with the expression signature would decrease the rate of undetected nodal metastases from 28% to 11% in early-stage OSCC. This should be sufficient to enable clinicians to refrain from elective neck treatment. A new clinical decision model that incorporates the expression signature is therefore proposed for testing in a prospective study, which could substantially improve treatment for this group of patients.