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Simple Summary: Rapidly growing neuroendocrine neoplasms (NEN) often defy easy classification by the pathologist. Machine learning approaches can improve the classification's accuracy, but these generally require large amounts of training data. As tumor-based training data will remain sparse for very rare malignancies, such as NEN from the pancreas, we aimed for a machine learning-aided classification on the basis of the tumors' similarity to non-transformed pancreatic cell types. We determined the relative contribution of the different healthy cell types to the transcriptome of each NEN and used the information to train a model for predicting the overall patient survival time, neoplastic grading, and carcinoma versus tumor subclassification. This approach does not use proliferation as a feature, since healthy pancreatic epithelial cell types do not proliferate. Hence, our approach is complementary to the established proliferation rate-based classification scheme, thereby providing additional criteria for a confident classification of ambiguous cases. Pancreatic neuroendocrine neoplasms (panNENs) are a rare yet diverse type of neoplasia whose precise clinical–pathological classification is frequently challenging. Since incorrect classifications can affect treatment decisions, additional tools which support the diagnosis, such as machine learning (ML) techniques, are critically needed but generally unavailable due to the scarcity of suitable ML training data for rare panNENs. Here, we demonstrate that a multi-step ML framework predicts clinically relevant panNEN characteristics while being exclusively trained on widely available data of a healthy origin. The approach classifies panNENs by deconvolving their transcriptomes into cell type proportions based on shared gene expression profiles with healthy pancreatic cell types. The deconvolution results were found to provide a prognostic value with respect to the prediction of the overall patient survival time, neoplastic grading, and carcinoma versus tumor subclassification. The performance with which a proliferation rate agnostic deconvolution ML model could predict the clinical characteristics was found to be comparable to that of a comparative baseline model trained on the proliferation rate-informed MKI67 levels. The approach is novel in that it complements established proliferation rate-oriented classification schemes whose results can be reproduced and further refined by differentiating between identically graded subgroups. By including non-endocrine cell types, the deconvolution approach furthermore provides an in silico quantification of panNEN dedifferentiation, optimizing it for challenging clinical classification tasks in more aggressive panNEN subtypes. [ABSTRACT FROM AUTHOR]

• Analysis of recurrent/metastatic head and neck cancer. • Feasibility of a 231-gene NanoString panel in determining the molecular subtypes. • Tumor budding prognostically significant in recurrent/metastatic state. We aimed to evaluate the applicability of a customized NanoString panel for molecular subtyping of recurrent or metastatic head and neck squamous cell carcinoma (R/M−HNSCC). Additionally, histological analyses were conducted, correlated with the molecular subtypes and tested for their prognostic value. We conducted molecular subtyping of R/M−HNSCC according to the molecular subtypes defined by Keck et al. For molecular analyses a 231 gene customized NanoString panel (the most accurately subtype defining genes, based on previous analyses) was applied to tumor samples from R/M−HNSCC patients that were treated in the CeFCiD trial (AIO/IAG-KHT trial 1108). A total of 130 samples from 95 patients were available for sequencing, of which 80 samples from 67 patients passed quality controls and were included in histological analyses. H&E stained slides were evaluated regarding distinct morphological patterns (e.g. tumor budding, nuclear size, stroma content). Determination of molecular subtypes led to classification of tumor samples as basal (n = 46, 45 %), inflamed/mesenchymal (n = 31, 30 %) and classical (n = 26, 25 %). Expression levels of Amphiregulin (AREG) were significantly higher for the basal and classical subtypes compared to the mesenchymal subtype. While molecular subtypes did not have an impact on survival, high levels of tumor budding were associated with poor outcomes. No correlation was found between molecular subtypes and histological characteristics. Utilizing the 231-gene NanoString panel we were able to determine the molecular subtype of R/M−HNSCC samples by the use of FFPE material. The value to stratify for different treatment options remains to be explored in the future. The prognostic value of tumor budding was underscored in this clinically well annotated cohort. [ABSTRACT FROM AUTHOR]

Simple Summary: Neuroendocrine tumors of the gastrointestinal tract (GEP-NEN) are a rare type of tumor with considerable variability in the course of disease, which makes clinical management particularly challenging. Biomarkers that are able to guide personalized treatment decisions would be of importance, but are not yet available. In this study, we demonstrate that tissue expression, as well as circulating levels of the cytokine Flt3L in advanced and aggressive tumors, predict survival and time to progression. Increased tumoral Flt3L was also associated with upregulation of genes related to immune activation, suggesting Flt3L as a surrogate marker of host anti-tumor immunity. Therefore, Flt3L measurements in serum may hold promise as a biomarker of disease outcome that could support personalized treatment decisions in GEP-NEN patients, potentially guiding researchers towards viable immunotherapies. Background: The clinical management of high-grade gastroenteropancreatic neuroendocrine neoplasms (GEP-NEN) is challenging due to disease heterogeneity, illustrating the need for reliable biomarkers facilitating patient stratification and guiding treatment decisions. FMS-like tyrosine kinase 3 ligand (Flt3L) is emerging as a prognostic or predictive surrogate marker of host tumoral immune response and might enable the stratification of patients with otherwise comparable tumor features. Methods: We evaluated Flt3L gene expression in tumor tissue as well as circulating Flt3L levels as potential biomarkers in a cohort of 54 patients with GEP-NEN. Results: We detected a prominent induction of Flt3L gene expression in individual G2 and G3 NEN, but not in G1 neuroendocrine tumors (NET). Flt3L mRNA expression levels in tumor tissue predicted the disease-related survival of patients with highly proliferative G2 and G3 NEN more accurately than the conventional criteria of grading or NEC/NET differentiation. High level Flt3L mRNA expression was associated with the increased expression of genes related to immunogenic cell death, lymphocyte effector function and dendritic cell maturation, suggesting a less tolerogenic (more proinflammatory) phenotype of tumors with Flt3L induction. Importantly, circulating levels of Flt3L were also elevated in high grade NEN and correlated with patients' progression-free and disease-related survival, thereby reflecting the results observed in tumor tissue. Conclusions: We propose Flt3L as a prognostic biomarker for high grade GEP-NEN, harnessing its potential as a marker of an inflammatory tumor microenvironment. Flt3L measurements in serum, which can be easily be incorporated into clinical routine, should be further evaluated to guide patient stratification and treatment decisions. [ABSTRACT FROM AUTHOR]

Detection of epithelial ovarian cancer (EOC) poses a critical medical challenge. However, novel biomarkers for diagnosis remain to be discovered. Therefore, innovative approaches are of the utmost importance for patient outcome. Here, we present a concept for blood-based biomarker discovery, investigating both epithelial and specifically stromal compartments, which have been neglected in search for novel candidates. We queried gene expression profiles of EOC including microdissected epithelium and adjacent stroma from benign and malignant tumours. Genes significantly differentially expressed within either the epithelial or the stromal compartments were retrieved. The expression of genes whose products are secreted yet absent in the blood of healthy donors were validated in tissue and blood from patients with pelvic mass by NanoString analysis. Results were confirmed by the comprehensive gene expression database, CSIOVDB (Ovarian cancer database of Cancer Science Institute Singapore). The top 25% of candidate genes were explored for their biomarker potential, and twelve were able to discriminate between benign and malignant tumours on transcript levels (p < 0.05). Among them T-cell differentiation protein myelin and lymphocyte (MAL), aurora kinase A (AURKA), stroma-derived candidates versican (VCAN), and syndecan-3 (SDC), which performed significantly better than the recently reported biomarker fibroblast growth factor 18 (FGF18) to discern malignant from benign conditions. Furthermore, elevated MAL and AURKA expression levels correlated significantly with a poor prognosis. We identified promising novel candidates and found the stroma of EOC to be a suitable compartment for biomarker discovery. [ABSTRACT FROM AUTHOR]

Cancer cell lines (CCL) are an integral part of modern cancer research but are susceptible to misidentification. The increasing popularity of sequencing technologies motivates the in-silico identification of CCLs based on their mutational fingerprint, but care must be taken when identifying heterogeneous data. We recently developed the proof-of-concept Uniquorn 1 method which could reliably identify heterogeneous sequencing data from selected sequencing technologies. Here we present Uniquorn 2, a generic and robust in-silico identification method for CCLs with DNA/RNA-seq and panel-seq information. We benchmarked Uniquorn 2 by cross-identifying 1612 RNA and 3596 panel-sized NGS profiles derived from 1516 CCLs, five repositories, four technologies and three major cancer panel-designs. Our method achieves an accuracy of 96% for RNA-seq and 95% for mixed DNA-seq and RNA-seq identification. Even for a panel of only 94 cancer-related genes, accuracy remains at 82% but decreases when using smaller panels. Uniquorn 2 is freely available as R-Bioconductor-package 'Uniquorn'. [ABSTRACT FROM AUTHOR]