Performance of Different Diagnostic PD-L1 Clones in Head and Neck Squamous Cell Carcinoma

Background: The approval of immune checkpoint inhibitors in combination with specific diagnostic biomarkers presents new challenges to pathologists as tumor tissue needs to be tested for expression of programmed death-ligand 1 (PD-L1) for a variety of indications. As there is currently no requirement to use companion diagnostic assays for PD-L1 testing in Germany different clones are used in daily routine. While the correlation of staining results has been tested in various entities, there is no data for head and neck squamous cell carcinomas (HNSCC) so far. Methods: We tested five different PD-L1 clones (SP263, SP142, E1L3N, 22-8, 22C3) on primary HNSCC tumor tissue of 75 patients in the form of tissue microarrays. Stainings of both immune and tumor cells were then assessed and quantified by pathologists to simulate real-world routine diagnostics. The results were analyzed descriptively and the resulting staining pattern across patients was further investigated by principal component analysis and non-negative matrix factorization clustering. Results: Percentages of positive immune and tumor cells varied greatly. Both the resulting combined positive score as well as the eligibility for certain checkpoint inhibitor regimens was therefore strongly dependent on the choice of the antibody. No relevant co-clustering and low similarity of relative staining patterns across patients was found for the different antibodies. Conclusions: Performance of different diagnostic anti PD-L1 antibody clones in HNSCC is less robust and interchangeable compared to reported data from other tumor entities. Determination of PD-L1 expression is critical for therapeutic decision making and may be aided by back-to-back testing of different PD-L1 clones

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

Disseminated ectopic pregnancy after salpingotomy in a 30‐year‐old patient

Abstract We present a case of a 30‐year old patient who devoloped a disseminated abdominal pregnancy after receiving a salpingotomy due to a prior tubal pregnancy

Comparison of manual and automated digital image analysis systems for quantification of cellular protein expression

Objective. Quantifying protein expression in immunohistochemically stained histological slides is an important tool for oncologic research. The use of computer-aided evaluation of IHC-stained slides significantly contributes to objectify measurements. Manual digital image analysis (mDIA) requires a userdependent annotation of the region of interest (ROI). Others have built-in machine learning algorithms with automated digital image analysis (aDIA) and can detect the ROIs automatically. We aimed to investigate the agreement between the results obtained by aDIA and those derived from mDIA systems. Methods. We quantified chromogenic intensity (CI) and calculated the positive index (PI) in cohorts of tissue microarrays (TMA) using mDIA and aDIA. To consider the different distributions of staining within cellular subcompartments and different tumor architecture our study encompassed nuclear and cytoplasmatic stainings in adenocarcinomas and squamous cell carcinomas. Results. Within all cohorts, we were able to show a high correlation between mDIA and aDIA for the CI (p<0.001) along with high agreement for the PI. Moreover, we were able to show that the cell detections of the programs were comparable as well and both proved to be reliable when compared to manual counting. Conclusion. mDIA and aDIA show a high correlation in acquired IHC data. Both proved to be suitable to stratify patients for evaluation with clinical data. As both produce the same level of information, aDIA might be preferable as it is time-saving, can easily be reproduced, and enables regular and efficient output in large studies in a reasonable time period

Therapy-related transcriptional subtypes in matched primary and recurrent head and neck cancer.

PURPOSE: The genetic relatedness between primary and recurrent head and neck squamous cell carcinomas (HNSCC) reflects the extent of heterogeneity and therapy-driven selection of tumor subpopulations. Yet, current treatment of recurrent HNSCC ignores the molecular characteristics of therapy-resistant tumor populations. EXPERIMENTAL DESIGN: From 150 tumors, 74 primary HNSCCs were RNA-sequenced and 38 matched primary/recurrent tumor pairs were both, whole-exome and RNA-sequenced. Transcriptome analysis determined the predominant classical (CL), basal (BA) and inflamed-mesenchymal (IMS) transcriptional subtypes according to an established classification. Genomic alterations and clonal compositions of tumors were evaluated from whole-exome data. RESULTS: While CL and IMS subtypes were more common in primary HNSCC with low recurrence rates, the BA subtype was more prevalent and stable in recurrent tumors. The BA subtype was associated with a transcriptional signature of partial epithelial-to-mesenchymal transition (p-emt) and early recurrence. In 44% of matched cases, the dominant subtype changed from primary to recurrent tumors, preferably from IMS to BA or CL. Gene set enrichment analysis identified upregulation of Hypoxia, p-emt and radiation resistance signatures and downregulation of tumor inflammation in recurrences compared to index tumors. A relevant subset of primary/recurrent tumor pairs presented no evidence for a common clonal origin. CONCLUSIONS: Our study showed a high degree of genetic and transcriptional heterogeneity between primary/recurrent tumors, suggesting therapy-related selection of a transcriptional subtype with characteristics unfavorable for therapy. We conclude that therapy decisions should be based on genetic and transcriptional characteristics of recurrences rather than primary tumors to enable optimally tailored treatment strategies

DNA methylation-based reclassification of olfactory neuroblastoma

Olfactory neuroblastoma/esthesioneuroblastoma (ONB) is an uncommon neuroectodermal neoplasm thought to arise from the olfactory epithelium. Little is known about its molecular pathogenesis. For this study, a retrospective cohort of n=66 tumor samples with the institutional diagnosis of ONB was analyzed by immunohistochemistry, genome-wide DNA methylation profiling, copy number analysis, and in a subset, next-generation panel sequencing of 560 tumor-associated genes. DNA methylation profiles were compared to those of relevant differential diagnoses of ONB. Unsupervised hierarchical clustering analysis of DNA methylation data revealed 4 subgroups among institutionally diagnosed ONB. The largest group (n=42, 64%, Core ONB) presented with classical ONB histology and no overlap with other classes upon methylation profiling-based t-distributed stochastic neighbor embedding (t-SNE) analysis. A second DNA methylation group (n=7, 11%) with CpG island methylator phenotype (CIMP) consisted of cases with strong expression of cytokeratin, no or scarce chromogranin A expression and IDH2 hotspot mutation in all cases. T-SNE analysis clustered these cases together with sinonasal carcinoma with IDH2 mutation. Four cases (6%) formed a small group characterized by an overall high level of DNA methylation, but without CIMP. The fourth group consisted of 13 cases that had heterogeneous DNA methylation profiles and strong cytokeratin expression in most cases. In t-SNE analysis these cases mostly grouped among sinonasal adenocarcinoma, squamous cell carcinoma and undifferentiated carcinoma. Copy number analysis indicated highly recurrent chromosomal changes among Core ONB with a high frequency of combined loss of chromosome 1-4, 8-10 and 12. NGS sequencing did not reveal highly recurrent mutations in ONB, with the only recurrently mutated genes being TP53 and DNMT3A. In conclusion, we demonstrate that institutionally diagnosed ONB are a heterogeneous group of tumors. Expression of cytokeratin, chromogranin A, the mutational status of IDH2 as well as DNA methylation patterns may greatly aid in the precise classification of ONB