Quantifying DNA replication speeds in single cells by scEdU-seq.

In a human cell, thousands of replication forks simultaneously coordinate duplication of the entire genome. The rate at which this process occurs might depend on the epigenetic state of the genome and vary between, or even within, cell types. To accurately measure DNA replication speeds, we developed single-cell 5-ethynyl-2'-deoxyuridine sequencing to detect nascent replicated DNA. We observed that the DNA replication speed is not constant but increases during S phase of the cell cycle. Using genetic and pharmacological perturbations we were able to alter this acceleration of replication and conclude that DNA damage inflicted by the process of transcription limits the speed of replication during early S phase. In late S phase, during which less-transcribed regions replicate, replication accelerates and approaches its maximum speed.

DNA Methylation Profiling Enables Accurate Classification of Nonductal Primary Pancreatic Neoplasms.

BACKGROUND & AIMS: Cytologic and histopathologic diagnosis of non-ductal pancreatic neoplasms can be challenging in daily clinical practice, whereas it is crucial for therapy and prognosis. The cancer methylome is successfully used as a diagnostic tool in other cancer entities. Here, we investigate if methylation profiling can improve the diagnostic work-up of pancreatic neoplasms. METHODS: DNA methylation data were obtained for 301 primary tumors spanning 6 primary pancreatic neoplasms and 20 normal pancreas controls. Neural Network, Random Forest, and extreme gradient boosting machine learning models were trained to distinguish between tumor types. Methylation data of 29 nonpancreatic neoplasms (n = 3708) were used to develop an algorithm capable of detecting neoplasms of non-pancreatic origin. RESULTS: After benchmarking 3 state-of-the-art machine learning models, the random forest model emerged as the best classifier with 96.9% accuracy. All classifications received a probability score reflecting the confidence of the prediction. Increasing the score threshold improved the random forest classifier performance up to 100% with 87% of samples with scores surpassing the cutoff. Using a logistic regression model, detection of nonpancreatic neoplasms achieved an area under the curve of >0.99. Analysis of biopsy specimens showed concordant classification with their paired resection sample. CONCLUSIONS: Pancreatic neoplasms can be classified with high accuracy based on DNA methylation signatures. Additionally, non-pancreatic neoplasms are identified with near perfect precision. In summary, methylation profiling can serve as a valuable adjunct in the diagnosis of pancreatic neoplasms with minimal risk for misdiagnosis, even in the pre-operative setting.

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.

Identification of T cell target antigens in glioblastoma stem-like cells using an integrated proteomics-based approach in patient specimens.

Glioblastoma (GBM) is a highly aggressive brain tumor and still remains incurable. Among others, an immature subpopulation of self-renewing and therapy-resistant tumor cells-often referred to as glioblastoma stem-like cells (GSCs)-has been shown to contribute to disease recurrence. To target these cells personalized immunotherapy has gained a lot of interest, e.g. by reactivating pre-existing anti-tumor immune responses against GSC antigens. To identify T cell targets commonly presented by GSCs and their differentiated counterpart, we used a proteomics-based separation of GSC proteins in combination with a T cell activation assay. Altogether, 713 proteins were identified by LC-ESI-MS/MS mass spectrometry. After a thorough filtering process, 32 proteins were chosen for further analyses. Immunogenicity of corresponding peptides was tested ex vivo. A considerable number of these antigens induced T cell responses in GBM patients but not in healthy donors. Moreover, most of them were overexpressed in primary GBM and also highly expressed in recurrent GBM tissues. Interestingly, expression of the most frequent T cell target antigens could also be confirmed in quiescent, slow-cycling GSCs isolated in high purity by the DEPArray technology. Finally, for a subset of these T cell target antigens, an association between expression levels and higher T cell infiltration as well as an increased expression of positive immune modulators was observed. In summary, we identified novel immunogenic proteins, which frequently induce tumor-specific T cell responses in GBM patients and were also detected in vitro in therapy-resistant quiescent, slow-cycling GSCs. Stable expression of these T cell targets in primary and recurrent GBM support their suitability for future clinical use.

LOC283731 promoter hypermethylation prognosticates survival after radiochemotherapy in IDH1 wild-type glioblastoma patients.

MGMT promoter methylation status is currently the only established molecular prognosticator in IDH wild-type glioblastoma multiforme (GBM). Therefore, we aimed to discover novel therapy-associated epigenetic biomarkers. After enrichment for hypermethylated fractions using methyl-CpG-immunoprecipitation (MCIp), we performed global DNA methylation profiling for 14 long-term (LTS; >36 months) and 15 short-term (STS; 6-10 months) surviving GBM patients. Even after exclusion of the G-CIMP phenotype, we observed marked differences between the LTS and STS methylome. A total of 1,247 probes in 706 genes were hypermethylated in LTS and 463 probes in 305 genes were found to be hypermethylated in STS patients (p values < 0.05, log2 fold change ± 0.5). We identified 13 differentially methylated regions (DMRs) with a minimum of four differentially methylated probes per gene. Indeed, we were able to validate a subset of these DMRs through a second, independent method (MassARRAY) in our LTS/STS training set (ADCY1, GPC3, LOC283731/ISLR2). These DMRs were further assessed for their prognostic capability in an independent validation cohort (n = 62) of non-G-CIMP GBMs from the TCGA. Hypermethylation of multiple CpGs mapping to the promoter region of LOC283731 correlated with improved patient outcome (p = 0.03). The prognostic performance of LOC283731 promoter hypermethylation was confirmed in a third independent study cohort (n = 89), and was independent of gender, performance (KPS) and MGMT status (p = 0.0485, HR = 0.63). Intriguingly, the prediction was most pronounced in younger GBM patients (<60 years). In conclusion, we provide compelling evidence that promoter methylation status of this novel gene is a prognostic biomarker in IDH1 wild-type/non-G-CIMP GBMs.

Spatial transcriptome analysis reveals Notch pathway-associated prognostic markers in IDH1 wild-type glioblastoma involving the subventricular zone.

BACKGROUND: The spatial relationship of glioblastoma (GBM) to the subventricular zone (SVZ) is associated with inferior patient survival. However, the underlying molecular phenotype is largely unknown. We interrogated an SVZ-dependent transcriptome and potential location-specific prognostic markers. METHODS: mRNA microarray data of a discovery set (n = 36 GBMs) were analyzed for SVZ-dependent gene expression and process networks using the MetaCore™ workflow. Differential gene expression was confirmed by qPCR in a validation set of 142 IDH1 wild-type GBMs that was also used for survival analysis. RESULTS: Microarray analysis revealed a transcriptome distinctive of SVZ+ GBM that was enriched for genes associated with Notch signaling. No overlap was found to The Cancer Genome Atlas's molecular subtypes. Independent validation of SVZ-dependent expression confirmed four genes with simultaneous prognostic impact: overexpression of HES4 (p = 0.034; HR 1.55) and DLL3 (p = 0.017; HR 1.61) predicted inferior, and overexpression of NTRK2 (p = 0.049; HR 0.66) and PIR (p = 0.025; HR 0.62) superior overall survival (OS). Additionally, overexpression of DLL3 was predictive of shorter progression-free survival (PFS) (p = 0.043; HR 1.64). Multivariate analysis revealed overexpression of HES4 to be independently associated with inferior OS (p = 0.033; HR 2.03), and overexpression of DLL3 with inferior PFS (p = 0.046; HR 1.65). CONCLUSIONS: We identified four genes with SVZ-dependent expression and prognostic significance, among those HES4 and DLL3 as part of Notch signaling, suggesting further evaluation of location-tailored targeted therapies.

Molecular profiling of long-term survivors identifies a subgroup of glioblastoma characterized by chromosome 19/20 co-gain.

Glioblastoma (GBM) is a devastating tumor and few patients survive beyond 3 years. Defining the molecular determinants underlying long-term survival is essential for insights into tumor biology and biomarker identification. We therefore investigated homogeneously treated, IDH (wt) long-term (LTS, n = 10) and short-term survivors (STS, n = 6) by microarray transcription profiling. While there was no association of clinical parameters and molecular subtypes with long-term survival, STS tumors were characterized by differential polarization of infiltrating microglia with predominance of the M2 phenotype detectable both on the mRNA and protein level. Furthermore, transcriptional signatures of LTS and STS predicted patient outcome in a large, IDH (wt) cohort (n = 468). Interrogation of overlapping genomic alterations identified concurrent gain of chromosomes 19 and 20 as a favorable prognostic marker. The strong association of this co-gain with survival was validated by aCGH in a second, independent cohort (n = 124). Finally, FISH and gene expression data revealed gains to constitute low-amplitude, clonal events with a strong impact on transcription. In conclusion, these findings provide important insights into the manipulation of the innate immune system by particularly aggressive GBM tumors. Furthermore, we genomically characterize a previously unknown, clinically relevant subgroup of glioblastoma, which can easily be identified through modern neuropathological workup.

Genome Methylation Accurately Predicts Neuroendocrine Tumor Origin: An Online Tool.

PURPOSE: The primary origin of neuroendocrine tumor metastases can be difficult to determine by histopathology alone, but is critical for therapeutic decision making. DNA methylation-based profiling is now routinely used in the diagnostic workup of brain tumors. This has been enabled by the availability of cost-efficient array-based platforms. We have extended these efforts to augment histopathologic diagnosis in neuroendocrine tumors. EXPERIMENTAL DESIGN: Methylation data was compiled for 69 small intestinal, pulmonary, and pancreatic neuroendocrine tumors. These data were used to build a ridge regression calibrated random forest classification algorithm (neuroendocrine neoplasm identifier, NEN-ID). The model was validated during 3 × 3 nested cross-validation and tested in a local and an external cohort (n = 198 cases). RESULTS: NEN-ID predicted the origin of tumor samples with high accuracy (>95%). In addition, the diagnostic approach was determined to be robust across a range of possible confounding experimental parameters, such as tumor purity and array quality. A software infrastructure and online user interface were built to make the model available to the scientific community. CONCLUSIONS: This DNA methylation-based prediction model can be used in the workup for patients with neuroendocrine tumors of unknown primary. To facilitate validation and clinical implementation, we provide a user-friendly, publicly available web-based version of NEN-ID.

Surfactant Expression Defines an Inflamed Subtype of Lung Adenocarcinoma Brain Metastases that Correlates with Prolonged Survival.

PURPOSE: To provide a better understanding of the interplay between the immune system and brain metastases to advance therapeutic options for this life-threatening disease. EXPERIMENTAL DESIGN: Tumor-infiltrating lymphocytes (TIL) were quantified by semiautomated whole-slide analysis in brain metastases from 81 lung adenocarcinomas. Multi-color staining enabled phenotyping of TILs (CD3, CD8, and FOXP3) on a single-cell resolution. Molecular determinants of the extent of TILs in brain metastases were analyzed by transcriptomics in a subset of 63 patients. Findings in lung adenocarcinoma brain metastases were related to published multi-omic primary lung adenocarcinoma The Cancer Genome Atlas data (n = 230) and single-cell RNA-sequencing (scRNA-seq) data (n = 52,698). RESULTS: TIL numbers within tumor islands was an independent prognostic marker in patients with lung adenocarcinoma brain metastases. Comparative transcriptomics revealed that expression of three surfactant metabolism-related genes (SFTPA1, SFTPB, and NAPSA) was closely associated with TIL numbers. Their expression was not only prognostic in brain metastasis but also in primary lung adenocarcinoma. Correlation with scRNA-seq data revealed that brain metastases with high expression of surfactant genes might originate from tumor cells resembling alveolar type 2 cells. Methylome-based estimation of immune cell fractions in primary lung adenocarcinoma confirmed a positive association between lymphocyte infiltration and surfactant expression. Tumors with a high surfactant expression displayed a transcriptomic profile of an inflammatory microenvironment. CONCLUSIONS: The expression of surfactant metabolism-related genes (SFTPA1, SFTPB, and NAPSA) defines an inflamed subtype of lung adenocarcinoma brain metastases characterized by high abundance of TILs in close vicinity to tumor cells, a prolonged survival, and a tumor microenvironment which might be more accessible to immunotherapeutic approaches.

Prognostic relevance of elevated pulmonary arterial pressure assessed non-invasively: Analysis in a large patient cohort with invasive measurements in near temporal proximity.

BACKGROUND: The clinical relevance of non-invasively derived pulmonary arterial pressure (PAP) by Doppler echocardiography (DE) has been questioned in the past. However, transthoracic echocardiography is used as a cornerstone examination for patients with dyspnea and suspected pulmonary hypertension (PH). This study aimed to evaluate the prognostic value of non-invasive assessed PAP in a large population of patients with known or suspected cardiopulmonary disease. METHODS: The analyses are based on data of patients of a tertiary cardiology center that received right heart catheterization (RHC) as well as non-invasively assessed PAP by DE within five days, and includes serological and clinical parameters in a retrospective follow-up for up to eight years. RESULTS: Of 1,237 patients, clinical follow-up was possible in 1,038 patients who were included in the statistical analysis. The mean-follow up time was 1,002 days. The composite endpoint of heart transplantation (HTx) or death occurred in n = 308 patients. Elevated PAP measured non-invasively as well as invasively had significant prognostic impact (hazard ratio (HR) 2.32; 95% confidence interval (CI) 1.78-3.04; χ2 = 37.9; p<0.001 versus HR 2.84; 95%CI 2.11-3.82; χ2 = 51.9; p<0.001, respectively). By multivariate analysis, NYHA functional class, N-terminal pro-brain natriuretic peptide, cardiac troponin T, left ventricular ejection fraction, and right ventricular dysfunction remained independently predictive. Incremental prognostic information in a multimodal approach was highly relevant. CONCLUSIONS: In this comprehensive study, elevated pulmonary arterial pressure measured by DE offers similar prognostic information on survival or need for HTx as right heart catheterization. Furthermore, the addition of functional capacity and serological biomarkers delivered incremental prognostic information.

Prognostic factors and long-term survival in surgically treated brain metastases from non-small cell lung cancer.

OBJECTIVE: Brain metastases (BMs) are the most common malignant brain tumors in adults. Despite multimodal treatment options such as microsurgery, radiotherapy and chemotherapy, prognosis still remains very poor. Non-small cell lung cancer (NSCLC) constitutes the most common source of brain metastases. In this study, prognostic factors in this patient population were identified through an in-depth analysis of clinical parameters of patients with BMs from NSCLC. PATIENTS AND METHODS: Clinical data of 114 NSCLC cancer patients who underwent surgery for BMs at the University Hospital Heidelberg were retrospectively reviewed for age, gender, type of treatment, time course of the disease, presence of neurologic symptoms, Karnofsky Performance Status (KPS), smoking history, presence of extracranial metastases at initial diagnosis of NSCLC, number, location and size of brain metastases. Univariate and multivariate survival analyses were performed using the Log-rank test and Cox' proportional hazard model, respectively. RESULTS: Median survival time from surgery for BMs was 11.2 months. 18.4% (21 of 114) patients were long-term survivors (>24 months; range 26.3-75.1 months). Age, gender, size and number of intracranial metastases were not significantly associated with patient survival. Univariate analysis identified complete resection, postoperative whole brain radiotherapy (WBRT) and a preoperative KPS of >80% as positive prognostic factors. Infratentorial location and presence of extracranial metastases were shown to be negative prognostic factors. Surgery for the primary tumor was associated with a superior patient outcome both in univariate and multivariate analyses. CONCLUSION: Our data strongly suggest that surgical treatment of the primary tumor and complete resection of brain metastases in NSCLC patients followed by WBRT improve survival. Moreover, long-term survivors (>2 years) were more frequent than previously reported.

Printed peptide arrays identify prognostic TNC serumantibodies in glioblastoma patients.

Liquid biopsies come of age offering unexploited potential to monitor and react to tumor evolution. We developed a cost-effective assay to non-invasively determine the immune status of glioblastoma (GBM) patients. Employing newly developed printed peptide microarrays we assessed the B-cell response against tumor-associated antigens (TAAs) in 214 patients. Firstly, sera of long-term (36+ months, LTS, n=10) and short-term (6-10 months, STS, n=14) surviving patients were screened for prognostic antibodies against 1745 13-mer peptides covering known TAAs (TNC, EGFR, GLEA2, PHF3, FABP5, MAGEA3). Next, survival associations were investigated in two retrospective independent multicenter validation sets (n=61, n=129, all IDH1-wildtype). Reliability of measurements was tested using a second array technology (spotted arrays). LTS/STS screening analyses identified 106 differential antibody responses. Evaluating the Top30 peptides in validation set 1 revealed three prognostic peptides. Prediction of TNC peptide VCEDGFTGPDCAE was confirmed in a second set (p=0.043, HR=0.66 [0.44-0.99]) and was unrelated to TNC protein expression. Median signals of printed arrays correlated with pre-synthesized spotted microarrays (p<0.0002, R=0.33). Multiple survival analysis revealed independence of age, gender, KPI and MGMT status. We present a novel peptide microarray immune assay that identified increased anti-TNC VCEDGFTGPDCAE serum antibody titer as a promising non-invasive biomarker for prolonged survival.