DNA methylation-based classification of sinonasal tumors

[Individualization and standardization in head and neck pathology]

Individualization and standardization are seemingly contradictory requirements in medicine. In the treatment of head and neck cancer, both terms have a direct influence on diagnostic procedures, which are usually carried out in pathology institutes. The current article examines the conflicting requirements arising from various technical analyses, regulatory requirements, structural changes due to digitalization, and the advent of personalized medicine. On the one hand, the goal is to promote interdisciplinary exchange by understanding the challenges and, on the other, to provide the otorhinolaryngologist with a practical understanding of the common and current pathological diagnostic tests. Using pathology as an example, it can be shown that standardization of procedures ultimately serves to improve individualized treatment. At the same time, however, the following challenges are also apparent: despite comprehensive regulations and a laboratory environment with digital support, standardization is very time consuming and costly. If similar standardization approaches are to be implemented in an operative environment such as, e.g., ENT surgery, the effort involved can be expected to be equivalent or higher due to the human factor.

Sex-specific DNA methylation marks associated with sex-biased risk of recurrence in unprovoked venous thromboembolism

BACKGROUND

Whether to stop oral anticoagulants after a first unprovoked venous thromboembolism (VTE) is challenging, partially due to an intriguingly higher risk of VTE recurrence (rVTE) in men after therapy discontinuation. DNA methylation (DNAm) differences between men and women might underlie this sex-biased rVTE risk difference.

OBJECTIVES

To investigate sex-specific associations between DNAm at cytosine-phosphate-guanine (CpG) sites and rVTE.

METHODS

In 417 unprovoked VTE patients, including 101 experiencing recurrences over a 5-year follow-up (REcurrent VEnous thromboembolism Risk Stratification Evaluation [REVERSE] I), we analyzed blood DNAm using the Illumina EPIC array and performed a sex-stratified epigenome-wide association study. We further examined 181 major provoked VTE patients, including 36 recurrences over a 14-year follow-up (the MARseille THrombosis Association [MARTHA]), to investigate whether DNAm is a risk factor for rVTE after anticoagulation therapy.

RESULTS

Hypomethylated CpGs at genes TBC1D22B-cg01060850 and ZHX2-cg07808424 in men and DIP2B-ch.12.1038646R and DENND3-cg03401656 in women were associated with rVTE at genome-wide level (P < 7x10-8). Though not statistically significant, DENND3-cg03401656 had the same direction of effect in MARTHA women. Sensitivity analysis confirmed the robustness of the estimates, including potential confounders, adaptations of the Cox model, non-Europeans, and proximal methylation quantitative trait loci in the association. The associated CpGs were situated at genes for membrane trafficking, corroborating the participation of Rab regulatory proteins in rVTE and transcription factors.

CONCLUSION

We identified DNAm marks as potential risk factors for sex-biased recurrence in unprovoked VTE. Further replication and experimental validation could refine our understanding of the regulation of the identified DNAm sites and help optimize personalized decision-making for long-term anticoagulation after a first VTE.

Nasal immature teratoma in an elderly patient: Clinicopathological and epigenetic analogies with central nervous system counterparts, alongside genomic divergences

Germ cell tumors (GCTs) are categorized as gonadal or extra-gonadal, based on the origin. Extra-gonadal GCTs predominantly manifest within the central nervous system (CNS), mediastinum, retroperitoneum, and sacrococcygeal region. These malignancies are most frequently diagnosed in the pediatric, adolescent, and young adult demographics. Incidences of GCT within the nasal cavity are notably scarce, with only six cases documented. This report details the case of a 70-year-old man who presented with a left nasal mass ultimately diagnosed as immature teratoma. A remarkable aspect of this case was the detection of SMARCA4 (BRG1) loss through immunohistochemical analysis. In addition, methylation profiling aligned this case with CNS GCTs, specifically those classified as non-germinomatous GCTs. This molecular characterization informed a tailored therapeutic strategy incorporating carboplatin and etoposide, alongside localized irradiation. This individualized treatment regimen achieved favorable outcomes, with the patient remaining recurrence free for over three years. This highlights the need for precise therapeutic approaches in the management of extragonadal GCTs, particularly those arising in atypical anatomical locations. The present case accentuates the significance of thorough diagnostic evaluations and customized treatment plans for rare GCT presentations. Further empirical and clinical investigations are warranted to enhance our understanding of and refine therapeutic protocols for such exceptional cases.

Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics

We have recently developed a machine learning classifier that enables fast, accurate, and affordable classification of brain tumors based on genome-wide DNA methylation profiles that is widely employed in the clinic. Neuro-oncology research would benefit greatly from understanding the underlying artificial intelligence decision process, which currently remains unclear. Here, we describe an interpretable framework to explain the classifier's decisions. We show that functional genomic regions of various sizes are predominantly employed to distinguish between different tumor classes, ranging from enhancers and CpG islands to large-scale heterochromatic domains. We detect a high degree of genomic redundancy, with many genes distinguishing individual tumor classes, explaining the robustness of the classifier and revealing potential targets for further therapeutic investigation. We anticipate that our resource will build up trust in machine learning in clinical settings, foster biomarker discovery and development of compact point-of-care assays, and enable further epigenome research of brain tumors. Our interpretable framework is accessible to the research community via an interactive web application ( https://hovestadtlab.shinyapps.io/shinyMNP/ ).

Recent developments in olfactory neuroblastoma research

PURPOSE OF REVIEW

Olfactory neuroblastoma (ONB) is a rare malignancy originating from olfactory neuroepithelial cells. Given its uncommon nature and complex clinical presentation, this comprehensive review highlights recent findings and treatment approaches for advancing clinical practice and research.

RECENT FINDINGS

Recent literature emphasizes significant advancements in the genomic profiling and molecular classification of ONB. Emerging targeted therapies include somatostatin analogs and programmed death-ligand 1 (PD-L1) inhibitors. In addition, the development of genetically engineered mouse models has provided valuable platforms for testing new treatment strategies, revealing similarities between ONB and small cell lung cancer, which may inform future therapeutic approaches.

SUMMARY

These findings have profound implications on clinical practice. Improved diagnostic accuracy through advanced imaging and genomic profiling in addition to identifying specific mutations for targeted therapy can lead to personalized treatments of patients with ONB. Developments in genetically engineered mouse models and multiinstitutional collaborative efforts are vital for advancing research and standardizing molecular testing. The integration of advanced imaging techniques, genomic profiling, and targeted therapies holds promise for improving patient outcomes and understanding this rare malignancy.

Deep learning-based classifier for carcinoma of unknown primary using methylation quantitative trait loci

Cancer of unknown primary (CUP) constitutes between 2% and 5% of human malignancies and is among the most common causes of cancer death in the United States. Brain metastases are often the first clinical presentation of CUP; despite extensive pathological and imaging studies, 20%-45% of CUP are never assigned a primary site. DNA methylation array profiling is a reliable method for tumor classification but tumor-type-specific classifier development requires many reference samples. This is difficult to accomplish for CUP as many cases are never assigned a specific diagnosis. Recent studies identified subsets of methylation quantitative trait loci (mQTLs) unique to specific organs, which could help increase classifier accuracy while requiring fewer samples. We performed a retrospective genome-wide methylation analysis of 759 carcinoma samples from formalin-fixed paraffin-embedded tissue samples using Illumina EPIC array. Utilizing mQTL specific for breast, lung, ovarian/gynecologic, colon, kidney, or testis (BLOCKT) (185k total probes), we developed a deep learning-based methylation classifier that achieved 93.12% average accuracy and 93.04% average F1-score across a 10-fold validation for BLOCKT organs. Our findings indicate that our organ-based DNA methylation classifier can assist pathologists in identifying the site of origin, providing oncologists insight on a diagnosis to administer appropriate therapy, improving patient outcomes.

A cost-effective and scalable approach for DNA extraction from FFPE tissues

Genomic profiling of cancer plays an increasingly vital role for diagnosis and therapy planning. In addition, research of novel diagnostic applications such as DNA methylation profiling requires large training and validation cohorts. Currently, most diagnostic cases processed in pathology departments are stored as formalin-fixed and paraffin embedded tissue blocks (FFPE). Consequently, there is a growing demand for high-throughput extraction of nucleic acids from FFPE tissue samples. While proprietary kits are available, they are expensive and offer little flexibility. Here, we present ht-HiTE, a high-throughput implementation of a recently published and highly efficient DNA extraction protocol. This approach enables manual and automated processing of 96-well plates with a liquid handler, offers two options for purification and utilizes off-the-shelf reagents. Finally, we show that NGS and DNA methylation microarray data obtained from DNA processed with ht-HiTE are of equivalent quality as compared to a manual, kit-based approach.

cIMPACT-NOW update 9: Recommendations on utilization of genome-wide DNA methylation profiling for central nervous system tumor diagnostics

Genome-wide DNA methylation signatures correlate with and distinguish central nervous system (CNS) tumor types. Since the publication of the initial CNS tumor DNA methylation classifier in 2018, this platform has been increasingly used as a diagnostic tool for CNS tumors, with multiple studies showing the value and utility of DNA methylation-based classification of CNS tumors. A Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) Working Group was therefore convened to describe the current state of the field and to provide advice based on lessons learned to date. Here, we provide recommendations for the use of DNA methylation-based classification in CNS tumor diagnostics, emphasizing the attributes and limitations of the modality. We emphasize that the methylation classifier is one diagnostic tool to be used alongside previously established diagnostic tools in a fully integrated fashion. In addition, we provide examples of the inclusion of DNA methylation data within the layered diagnostic reporting format endorsed by the World Health Organization (WHO) and the International Collaboration on Cancer Reporting. We emphasize the need for backward compatibility of future platforms to enable accumulated data to be compatible with new versions of the array. Finally, we outline the specific connections between methylation classes and CNS WHO tumor types to aid in the interpretation of classifier results. It is hoped that this update will assist the neuro-oncology community in the interpretation of DNA methylation classifier results to facilitate the accurate diagnosis of CNS tumors and thereby help guide patient management.

Integrated Proteomics and Machine Learning Approach Reveals PYCR1 as a Novel Biomarker to Predict Prognosis of Sinonasal Squamous Cell Carcinoma

Sinonasal squamous cell carcinoma (SNSCC) is a rare tumor with a high 5-year mortality rate. However, proteomic technologies have not yet been utilized to identify SNSCC-associated proteins, which could be used as biomarkers. In this study, we aimed to discover a biomarker to predict SNSCC patients using proteomic analysis integrated with machine learning models. Support vector machine (SVM), logistic regression (LR), random forest (RF), and gradient boost (GB) classifiers were developed to predict SNSCC based on proteomic profiles of SNSCC compared with nasal polyps (NP) as control. Seventeen feature proteins were found in all models, indicating possible biomarkers for SNSCC. Analysis of gene expression across multiple cancer types and their associations with cancer stage and patient survival in the TCGA-HNSC dataset identified a PYCR1 and MYO1B gene that could be a potential tumor-associated marker. The expression of PYCR1 was confirmed by RT-qPCR in SNSCC tissues, and its high expression was associated with poor overall survival, indicating PYCR1 as a potential tumor-associated biomarker to predict the prognosis of SNSCC.

Outcomes of SMARCB1-deficient sinonasal carcinoma: Largest single-center cross-sectional study

BACKGROUND

We evaluate outcomes of SMARCB1-deficient sinonasal carcinomas in the largest single-institution study.

METHODS

Retrospective cross-sectional study of patients with SMARCB1-deficient sinonasal carcinoma between 1998 and 2024. Disease-specific survival (DSS) and recurrence-free probability (RFP) at 1 and 5 years were measured by Kaplan-Meier method.

RESULTS

There were 47 patients with a median age of 53. Initial pathological diagnosis was altered in 33%. Twelve (34%) patients received neoadjuvant chemotherapy, with one partial response. Curative surgical approach was undertaken in 73%. Definitive chemoradiation was administered in 20%. DSS at 1 and 5 years was 93% and 45%, respectively. RFP at 1 and 5 years was 73% and 33%, respectively. On multivariate analysis, cranial nerve involvement (p = 0.01 for DSS) remained significantly worse for DSS and overall survival.

CONCLUSIONS

SMARCB1-deficient tumors had limited response to neoadjuvant chemotherapy. Cranial nerve involvement was associated with worse prognosis. Optimal treatment is unclear. Surgery should be offered to patients with resectable disease.

DNA Methylation Profiling of Salivary Gland Tumors Supports and Expands Conventional Classification

Tumors of the major and minor salivary glands histologically encompass a diverse and partly overlapping spectrum of frequent diagnostically challenging neoplasms. Despite recent advances in molecular testing and the identification of tumor-specific mutations or gene fusions, there is an unmet need to identify additional diagnostic biomarkers for entities lacking specific alterations. In this study, we collected a comprehensive cohort of 363 cases encompassing 20 different salivary gland tumor entities and explored the potential of DNA methylation to classify these tumors. We were able to show that most entities show specific epigenetic signatures and present a machine learning algorithm that achieved a mean balanced accuracy of 0.991. Of note, we showed that cribriform adenocarcinoma is epigenetically distinct from classical polymorphous adenocarcinoma, which could support risk stratification of these tumors. Myoepithelioma and pleomorphic adenoma form a uniform epigenetic class, supporting the theory of a single entity with a broad but continuous morphologic spectrum. Furthermore, we identified a histomorphologically heterogeneous but epigenetically distinct class that could represent a novel tumor entity. In conclusion, our study provides a comprehensive resource of the DNA methylation landscape of salivary gland tumors. Our data provide novel insight into disputed entities and show the potential of DNA methylation to identify new tumor classes. Furthermore, in future, our machine learning classifier could support the histopathologic diagnosis of salivary gland tumors.

Olfactory Neuroblastoma and Olfactory Carcinoma: A Practical Review

In the sinonasal tract, two tumor types are defined by neuroectodermal differentiation. Olfactory neuroblastoma (ONB), the traditional member of this category, is a keratin-negative neuroectodermal neoplasm that has lobulated architecture and variable neurofibrillary matrix. Olfactory carcinoma (OC), a newly-recognized diagnosis in the sinonasal tract, has keratin-positive neuroectodermal elements frequently intermixed with well-formed glands. These two neuroectodermal entities can show substantial overlap with other sinonasal small round blue cell tumors that express neuroendocrine markers. This review provides a practical overview of the key clinical and diagnostic features of ONB and OC and their differential diagnosis.

Characterization of Somatostatin Receptor 2 Gene Expression and Immune Landscape in Sinonasal Malignancies

Olfactory neuroblastoma (ONB), sinonasal undifferentiated carcinoma (SNUC), and sinonasal neuroendocrine carcinoma (SNEC) are rare malignancies arising from the sinonasal tract with limited therapeutic options. The expression of the somatostatin receptor 2 gene (SSTR2), which is expressed in other neuroendocrine neoplasms and is therapeutically actionable, has been reported in these tumors. Here, we analyzed SSTR2 gene expression and its associations with genomic features, established biomarkers predicting of immune response, and the tumor immune microenvironment in a cohort of ONB, SNUC, and SNEC tumor samples (26, 13, and 8 samples, respectively) from a real-world database. SSTR2 gene expression was high in neural-type ONB and low in basal-type ONB and in most of the SNUC and SNEC cases; there was no difference in expression between primary and metastatic tumors. The T cell-inflamed (TCI) score analysis classified 38.5% of SNUC cases as T cell-inflamed compared to only 3.9% of ONB and 0% of SNEC cases; 26.9% of ONB cases were classified as intermediate TCI; and SNEC had the lowest relative immune cell infiltration by deconvolution. In high SSTR2-expressing ONB, there was a higher proportion of infiltrating of Natural Killer cells and dendritic cells by deconvolution. Additionally, high SSTR2-expressing ONB was enriched for proliferation pathways, including E2F and Myc targets and G2M checkpoints. In conclusion, our findings delineate significant differences between these three types of sinonasal malignancies that were examined. In ONB, relative to SNUC and SNEC, the SSTR2 expression profile, combined with its immune profiles, indicates potential novel therapeutic strategies and combinations for this unmet clinical need. Conversely, the inflammatory microenvironment of SNUC may be targetable using immuno-oncologic therapies.

DNA methylation classifier to diagnose pancreatic ductal adenocarcinoma metastases from different anatomical sites

BACKGROUND

We have recently constructed a DNA methylation classifier that can discriminate between pancreatic ductal adenocarcinoma (PAAD) liver metastasis and intrahepatic cholangiocarcinoma (iCCA) with high accuracy (PAAD-iCCA-Classifier). PAAD is one of the leading causes of cancer of unknown primary and diagnosis is based on exclusion of other malignancies. Therefore, our focus was to investigate whether the PAAD-iCCA-Classifier can be used to diagnose PAAD metastases from other sites.

METHODS

For this scope, the anomaly detection filter of the initial classifier was expanded by 8 additional mimicker carcinomas, amounting to a total of 10 carcinomas in the negative class. We validated the updated version of the classifier on a validation set, which consisted of a biological cohort (n = 3579) and a technical one (n = 15). We then assessed the performance of the classifier on a test set, which included a positive control cohort of 16 PAAD metastases from various sites and a cohort of 124 negative control samples consisting of 96 breast cancer metastases from 18 anatomical sites and 28 carcinoma metastases to the brain.

RESULTS

The updated PAAD-iCCA-Classifier achieved 98.21% accuracy on the biological validation samples, and on the technical validation ones it reached 100%. The classifier also correctly identified 15/16 (93.75%) metastases of the positive control as PAAD, and on the negative control, it correctly classified 122/124 samples (98.39%) for a 97.85% overall accuracy on the test set. We used this DNA methylation dataset to explore the organotropism of PAAD metastases and observed that PAAD liver metastases are distinct from PAAD peritoneal carcinomatosis and primary PAAD, and are characterized by specific copy number alterations and hypomethylation of enhancers involved in epithelial-mesenchymal-transition.

CONCLUSIONS

The updated PAAD-iCCA-Classifier (available at https://classifier.tgc-research.de/ ) can accurately classify PAAD samples from various metastatic sites and it can serve as a diagnostic aid.

Update on Sinonasal Tract Malignancies: Advances in Diagnostic Modalities

CONTEXT.—

Sinonasal tract malignancies are rare cancers with frequent morphologic overlap. Given the similar histologic profiles seen in many of these entities, they often present a diagnostic challenge to the practicing pathologist.

OBJECTIVE.—

To provide a streamlined algorithm using histologic clues, immunohistochemical profiles, and molecular assays to aid in diagnosis of these lesions.

DATA SOURCES.—

Sources were the World Health Organization Tumor Classification, literature review, and institutional experience.

CONCLUSIONS.—

Although many sinonasal tract malignancies show similar histology, distinct immunohistochemical and molecular profiles can help parse out differences, thereby facilitating diagnosis for the pathologist.

Adenoid cystic carcinoma: insights from molecular characterization and therapeutic advances

Adenoid cystic carcinoma (ACC) is a malignant tumor primarily originating from the salivary glands, capable of affecting multiple organs. Although ACC typically exhibits slow growth, it is notorious for its propensity for neural invasion, local recurrence, and distant metastasis, making it a particularly challenging cancer to treat. The complexity of ACC's histological and molecular features poses significant challenges to current treatment modalities, which often show limited effectiveness. Recent advancements in single-cell RNA-sequencing (scRNA-seq) have begun to unravel unprecedented insights into the heterogeneity and subpopulation diversity within ACC, revealing distinct cellular phenotypes and origins. This review delves into the intricate pathological and molecular characteristics of ACC, focusing on recent therapeutic advancements. We particularly emphasize the insights gained from scRNA-seq studies that shed light on the cellular landscape of ACC, underscoring its heterogeneity and pathobiology. Moreover, by integrating analyses from public databases, this review proposes novel perspectives for advancing treatment strategies in ACC. This review contributes to the academic understanding of ACC by proposing novel therapeutic approaches informed by cutting-edge molecular insights, paving the way for more effective, personalized therapeutic approaches for this challenging malignancy.

Targeting Isocitrate Dehydrogenase (IDH) in Solid Tumors: Current Evidence and Future Perspectives

The isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) enzymes are involved in key metabolic processes in human cells, regulating differentiation, proliferation, and oxidative damage response. IDH mutations have been associated with tumor development and progression in various solid tumors such as glioma, cholangiocarcinoma, chondrosarcoma, and other tumor types and have become crucial markers in molecular classification and prognostic assessment. The intratumoral and serum levels of D-2-hydroxyglutarate (D-2-HG) could serve as diagnostic biomarkers for identifying IDH mutant (IDHmut) tumors. As a result, an increasing number of clinical trials are evaluating targeted treatments for IDH1/IDH2 mutations. Recent studies have shown that the focus of these new therapeutic strategies is not only the neomorphic activity of the IDHmut enzymes but also the epigenetic shift induced by IDH mutations and the potential role of combination treatments. Here, we provide an overview of the current knowledge about IDH mutations in solid tumors, with a particular focus on available IDH-targeted treatments and emerging results from clinical trials aiming to explore IDHmut tumor-specific features and to identify the clinical benefit of IDH-targeted therapies and their combination strategies. An insight into future perspectives and the emerging roles of circulating biomarkers and radiomic features is also included.

Application of machine learning for high-throughput tumor marker screening

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.

Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning

Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven means to enhance diagnostic accuracy but are also time consuming and not widely available. Here, to address these limitations, we developed Deep lEarning from histoPathoLOgy and methYlation (DEPLOY), a deep learning model that classifies CNS tumors to ten major categories from histopathology. DEPLOY integrates three distinct components: the first classifies CNS tumors directly from slide images ('direct model'), the second initially generates predictions for DNA methylation beta values, which are subsequently used for tumor classification ('indirect model'), and the third classifies tumor types directly from routinely available patient demographics. First, we find that DEPLOY accurately predicts beta values from histopathology images. Second, using a ten-class model trained on an internal dataset of 1,796 patients, we predict the tumor categories in three independent external test datasets including 2,156 patients, achieving an overall accuracy of 95% and balanced accuracy of 91% on samples that are predicted with high confidence. These results showcase the potential future use of DEPLOY to assist pathologists in diagnosing CNS tumors within a clinically relevant short time frame.

Methylation-Based Characterization of a New IDH2 Mutation in Sinonasal Undifferentiated Carcinoma

Mutations affecting codon 172 of the isocitrate dehydrogenase 2 (IDH2) gene define a subgroup of sinonasal undifferentiated carcinomas (SNUCs) with a relatively favorable prognosis and a globally hypermethylated phenotype. They are also recurrent (along with IDH1 mutations) in gliomas, acute myeloid leukemia, and intrahepatic cholangiocarcinoma. Commonly reported mutations, all associated with aberrant IDH2 enzymatic activity, include R172K, R172S, R172T, R172G, and R172M. We present a case of SNUC with a never-before-described IDH2 mutation, R172A. Our report compares the methylation pattern of our sample to other cases from the Gene Expression Omnibus database. Hierarchical clustering suggests a strong association between our sample and other IDH-mutant SNUCs and a clear distinction between sinonasal normal tissues and tumors. Principal component analysis (PCA), using 100 principal components explaining 94.5% of the variance, showed the position of our sample to be within 1.02 standard deviation of the other IDH-mutant SNUCs. A molecular modeling analysis of the IDH2 R172A versus other R172 variants provides a structural explanation to how they affect the protein active site. Our findings thus suggest that the R172A mutation in IDH2 confers a gain of function similar to other R172 mutations in IDH2, resulting in a similar hypermethylated profile.

Targeting sinonasal undifferentiated carcinoma with a combinatory immunotherapy approach

PURPOSE

Sinonasal undifferentiated carcinoma (SNUC) is a rare, aggressive malignancy of the sinonasal cavity with poor prognosis and limited treatment options. To investigate the potential for SNUC sensitivity to combinatory immunotherapy, we performed in vitro studies with SNUC cell lines and used multi-spectral immunofluorescence to characterize the in vivo patient SNUC tumor immune microenvironment (TIME).

EXPERIMENTAL DESIGN

Human-derived SNUC cell lines were used for in vitro studies of tumor cell susceptibility to natural killer (NK) cell-based immunotherapeutic strategies. Tumor samples from 14 treatment naïve SNUC patients were examined via multi-spectral immunofluorescence and clinical correlations assessed.

RESULTS

Anti-PD-L1 blockade enhanced NK cell lysis of SNUC cell lines ∼5.4 fold (P ≤ 0.0001). This effect was blocked by a CD16 neutralizing antibody demonstrating activity through an antibody-dependent cellular cytotoxicity (ADCC) mediated pathway. ADCC-dependent lysis of SNUC cells was further enhanced by upregulation of PD-L1 on tumor cells by exogenous interferon-gamma (IFN-γ) administration or interleukin-15 (IL-15) stimulated IFN-γ release from NK cells. Combination treatment with anti-PD-L1 blockade and IL-15 superagonism enhanced NK-cell killing of SNUC cells 9.6-fold (P ≤ 0.0001). Untreated SNUC patient tumor samples were found to have an NK cell infiltrate and PD-L1+ tumor cells at a median of 5.4 cells per mm2. A striking 55.7-fold increase in CKlow tumor cell/NK cell interactions was observed in patients without disease recurrence after treatment (P = 0.022). Patients with higher CD3+CD8+ in the stroma had a significantly improved 5-year overall survival (P = 0.0029) and a significant increase in CKlow tumor cell/CD8+ cytotoxic T cell interactions was noted in long-term survivors (P = 0.0225).

CONCLUSION

These data provide the pre-clinical rationale for ongoing investigation into combinatory immunotherapy approaches for SNUC.

Recurrent Wnt Pathway and ARID1A Alterations in Sinonasal Olfactory Carcinoma

Sinonasal tumors with neuroepithelial differentiation, defined by neuroectodermal elements reminiscent of olfactory neuroblastoma (ONB) and epithelial features such as keratin expression or gland formation, are a diagnostically challenging group that has never been formally included in sinonasal tumor classifications. Recently, we documented that most of these neuroepithelial neoplasms have distinctive histologic and immunohistochemical findings and proposed the term "olfactory carcinoma" to describe these tumors. However, the molecular characteristics of olfactory carcinoma have not yet been evaluated. In this study, we performed targeted molecular profiling of 23 sinonasal olfactory carcinomas to further clarify their pathogenesis and classification. All tumors included in this study were composed of high-grade neuroectodermal cells that were positive for pankeratin and at least 1 specific neuroendocrine marker. A significant subset of cases also displayed rosettes and neurofibrillary matrix, intermixed glands with variable cilia, peripheral p63/p40 expression, and S100 protein-positive sustentacular cells. Recurrent oncogenic molecular alterations were identified in 20 tumors, including Wnt pathway alterations affecting CTNNB1 (n = 8) and PPP2R1A (n = 2), ARID1A inactivation (n = 5), RUNX1 mutations (n = 3), and IDH2 hotspot mutations (n = 2). Overall, these findings do demonstrate the presence of recurrent molecular alterations in olfactory carcinoma, although this group of tumors does not appear to be defined by any single mutation. Minimal overlap with alterations previously reported in ONB also adds to histologic and immunohistochemical separation between ONB and olfactory carcinoma. Conversely, these molecular findings enhance the overlap between olfactory carcinoma and sinonasal neuroendocrine carcinomas. A small subset of neuroepithelial tumors might better fit into the superseding molecular category of IDH2-mutant sinonasal carcinoma. At this point, sinonasal neuroendocrine and neuroepithelial tumors may best be regarded as a histologic and molecular spectrum that includes core groups of ONB, olfactory carcinoma, neuroendocrine carcinoma, and IDH2-mutant sinonasal carcinoma.

Multi-Omics Integration for Liver Cancer Using Regression Analysis

Genetic biomarkers have played a pivotal role in the classification, prognostication, and guidance of clinical cancer therapies. Large-scale and multi-dimensional analyses of entire cancer genomes, as exemplified by projects like The Cancer Genome Atlas (TCGA), have yielded an extensive repository of data that holds the potential to unveil the underlying biology of these malignancies. Mutations stand out as the principal catalysts of cellular transformation. Nonetheless, other global genomic processes, such as alterations in gene expression and chromosomal re-arrangements, also play crucial roles in conferring cellular immortality. The incorporation of multi-omics data specific to cancer has demonstrated the capacity to enhance our comprehension of the molecular mechanisms underpinning carcinogenesis. This report elucidates how the integration of comprehensive data on methylation, gene expression, and copy number variations can effectively facilitate the unsupervised clustering of cancer samples. We have identified regressors that can effectively classify tumor and normal samples with an optimal integration of RNA sequencing, DNA methylation, and copy number variation while also achieving significant p-values. Further, these regressors were trained using linear and logistic regression with k-means clustering. For comparison, we employed autoencoder- and stacking-based omics integration and computed silhouette scores to evaluate the clusters. The proof of concept is illustrated using liver cancer data. Our analysis serves to underscore the feasibility of unsupervised cancer classification by considering genetic markers beyond mutations, thereby emphasizing the clinical relevance of additional global cellular parameters that contribute to the transformative process in cells. This work is clinically relevant because changes in gene expression and genomic re-arrangements have been shown to be signatures of cellular transformation across cancers, as well as in liver cancers.

EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics

DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data. Given these diverse approaches to methylation profiling, to date, there is no single tool that allows (1) classification and interpretation of microarray, nanopore and parallel sequencing data, (2) direct control of nanopore sequencers, and (3) the integration of microarray-based methylation reference data. Furthermore, no software capable of entirely running in routine diagnostic laboratory environments lacking high-performance computing and network infrastructure exists. To overcome these shortcomings, we present EpiDiP/NanoDiP as an open-source DNA methylation and copy number profiling suite, which has been benchmarked against an established supervised machine learning approach using in-house routine diagnostics data obtained between 2019 and 2021. Running locally on portable, cost- and energy-saving system-on-chip as well as gpGPU-augmented edge computing devices, NanoDiP works in offline mode, ensuring data privacy. It does not require the rigid training data annotation of supervised approaches. Furthermore, NanoDiP is the core of our public, free-of-charge EpiDiP web service which enables comparative methylation data analysis against an extensive reference data collection. We envision this versatile platform as a useful resource not only for neuropathologists and surgical pathologists but also for the tumour epigenetics research community. In daily diagnostic routine, analysis of native, unfixed biopsies by NanoDiP delivers molecular tumour classification in an intraoperative time frame.

Integrated Molecular and Histological Insights for Targeted Therapies in Mesenchymal Sinonasal Tract Tumors

PURPOSE OF REVIEW

This review aims to provide a comprehensive overview of mesenchymal sinonasal tract tumors (STTs), a distinct subset of STTs. Despite their rarity, mesenchymal STTs represent a unique clinical challenge, characterized by their rarity, often slow progression, and frequently subtle or overlooked symptoms. The complex anatomy of the sinonasal area, which includes critical structures such as the orbit, brain, and cranial nerves, further complicates surgical treatment options. This underscores an urgent need for more advanced and specialized therapeutic approaches.

RECENT FINDINGS

Advancements in molecular diagnostics, particularly in next-generation sequencing, have significantly enhanced our understanding of STTs. Consequently, the World Health Organization has updated its tumor classification to better reflect the distinct histological and molecular profiles of these tumors, as well as to categorize mesenchymal STTs with greater accuracy. The growing understanding of the molecular characteristics of mesenchymal STTs opens new possibilities for targeted therapeutic interventions, marking a significant shift in treatment paradigms. This review article concentrates on mesenchymal STTs, specifically addressing sinonasal tract angiofibroma, sinonasal glomangiopericytoma, biphenotypic sinonasal sarcoma, and skull base chordoma. These entities are marked by unique histopathological and molecular features, which challenge conventional treatment approaches and simultaneously open avenues for novel targeted therapies. Our discussion is geared towards delineating the molecular underpinnings of mesenchymal STTs, with the objective of enhancing therapeutic strategies and addressing the existing shortcomings in the management of these intricate tumors.

[Explainable artificial intelligence in pathology]

With the advancements in precision medicine, the demands on pathological diagnostics have increased, requiring standardized, quantitative, and integrated assessments of histomorphological and molecular pathological data. Great hopes are placed in artificial intelligence (AI) methods, which have demonstrated the ability to analyze complex clinical, histological, and molecular data for disease classification, biomarker quantification, and prognosis estimation. This paper provides an overview of the latest developments in pathology AI, discusses the limitations, particularly concerning the black box character of AI, and describes solutions to make decision processes more transparent using methods of so-called explainable AI (XAI).

Tissue of origin prediction for cancer of unknown primary using a targeted methylation sequencing panel

RATIONALE

Cancer of unknown primary (CUP) is a group of rare malignancies with poor prognosis and unidentifiable tissue-of-origin. Distinct DNA methylation patterns in different tissues and cancer types enable the identification of the tissue of origin in CUP patients, which could help risk assessment and guide site-directed therapy.

METHODS

Using genome-wide DNA methylation profile datasets from The Cancer Genome Atlas (TCGA) and machine learning methods, we developed a 200-CpG methylation feature classifier for CUP tissue of origin prediction (MFCUP). MFCUP was further validated with public-available methylation array data of 2977 specimens and targeted methylation sequencing of 78 Formalin-fixed paraffin-embedded (FFPE) samples from a single center.

RESULTS

MFCUP achieved an accuracy of 97.2% in a validation cohort (n = 5923) representing 25 cancer types. When applied to an Infinium 450 K array dataset (n = 1052) and an Infinium EPIC (850 K) array dataset (n = 1925), MFCUP achieved an overall accuracy of 93.4% and 84.8%, respectively. Based on MFCUP, we established a targeted bisulfite sequencing panel and validated it with FFPE sections from 78 patients of 20 cancer types. This methylation sequencing panel correctly identified tissue of origin in 88.5% (69/78) of samples. We also found that the methylation levels of specific CpGs can distinguish one cancer type from others, indicating their potential as biomarkers for cancer diagnosis and screening.

CONCLUSION

Our methylation-based cancer classifier and targeted methylation sequencing panel can predict tissue of origin in diverse cancer types with high accuracy.

Toward Explainable Artificial Intelligence for Precision Pathology

The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done.

Characterization of a Preclinical In Vitro Model Derived from a SMARCA4-Mutated Sinonasal Teratocarcinosarcoma

Sinonasal teratocarcinosarcoma (TCS) is a rare tumor that displays a variable histology with admixtures of epithelial, mesenchymal, neuroendocrine and germ cell elements. Facing a very poor prognosis, patients with TCS are in need of new options for treatment. Recently identified recurrent mutations in SMARCA4 may serve as target for modern therapies with EZH1/2 and CDK4/6 inhibitors. Here, we present the first in vitro cell line TCS627, established from a previously untreated primary TCS originating in the ethmoid sinus with invasion into the brain. The cultured cells expressed immunohistochemical markers, indicating differentiation of epithelial, neuroepithelial, sarcomatous and teratomatous components. Whole-exome sequencing revealed 99 somatic mutations including SMARCA4, ARID2, TET2, CDKN2A, WNT7A, NOTCH3 and STAG2, all present both in the primary tumor and in the cell line. Focusing on mutated SMARCA4 as the therapeutic target, growth inhibition assays showed a strong response to the CDK4/6 inhibitor palbociclib, but much less to the EZH1/2 inhibitor valemetostat. In conclusion, cell line TCS627 carries both histologic and genetic features characteristic of TCS and is a valuable model for both basic research and preclinical testing of new therapeutic options for treatment of TCS patients.

EUSICA/COST IMMUNO-model workshop fostering collaboration to advance sinonasal cancer research: A meeting report

Sinonasal cancer is a clinically and histologically heterogeneous group of rare tumors with generally poor clinical outcomes. Their low incidence hampers the advancement of clinical management as well as translational research, and calls for multicenter and multinational collaboration between physicians and researchers. This report describes the proceedings of a two-day conference organized by the European Network for Sinonasal Cancer Research (EUSICA) and COST Action 'IMMUNO-model', fostering such collaboration and focusing on preclinical tumor and immuno models, surgical and radio-oncological treatments, core facilities for genetic characterization and molecular tumor classification, and cancer registry.

Precision cancer medicine: Concepts, current practice, and future developments

Precision cancer medicine is a multidisciplinary team effort that requires involvement and commitment of many stakeholders including the society at large. Building on the success of significant advances in precision therapy for oncological patients over the last two decades, future developments will be significantly shaped by improvements in scalable molecular diagnostics in which increasingly complex multilayered datasets require transformation into clinically useful information guiding patient management at fast turnaround times. Adaptive profiling strategies involving tissue- and liquid-based testing that account for the immense plasticity of cancer during the patient's journey and also include early detection approaches are already finding their way into clinical routine and will become paramount. A second major driver is the development of smart clinical trials and trial concepts which, complemented by real-world evidence, rapidly broaden the spectrum of therapeutic options. Tight coordination with regulatory agencies and health technology assessment bodies is crucial in this context. Multicentric networks operating nationally and internationally are key in implementing precision oncology in clinical practice and support developing and improving the ecosystem and framework needed to turn invocation into benefits for patients. The review provides an overview of the diagnostic tools, innovative clinical studies, and collaborative efforts needed to realize precision cancer medicine.

[Molecular tumor diagnostics as the driving force behind precision oncology]

Molecular pathological diagnostics plays a central role in personalized oncology and requires multidisciplinary teamwork. It is just as relevant for the individual patient who is being treated with an approved therapy method or an individual treatment attempt as it is for prospective clinical studies that require the identification of specific therapeutic target structures or complex biomarkers for study inclusion. It is also of crucial importance for the generation of real-world data, which is becoming increasingly important for drug development. Future developments will be significantly shaped by improvements in scalable molecular diagnostics, in which increasingly complex and multi-layered data sets must be quickly converted into clinically useful information. One focus will be on the development of adaptive diagnostic strategies in order to be able to depict the enormous plasticity of a cancer disease over time.

DNA methylation-based classifier differentiates intrahepatic pancreato-biliary tumours

BACKGROUND

Differentiating intrahepatic cholangiocarcinomas (iCCA) from hepatic metastases of pancreatic ductal adenocarcinoma (PAAD) is challenging. Both tumours have similar morphological and immunohistochemical pattern and share multiple driver mutations. We hypothesised that DNA methylation-based machine-learning algorithms may help perform this task.

METHODS

We assembled genome-wide DNA methylation data for iCCA (n = 259), PAAD (n = 431), and normal bile duct (n = 70) from publicly available sources. We split this cohort into a reference (n = 399) and a validation set (n = 361). Using the reference cohort, we trained three machine learning models to differentiate between these entities. Furthermore, we validated the classifiers on the technical validation set and used an internal cohort (n = 72) to test our classifier.

FINDINGS

On the validation cohort, the neural network, support vector machine, and the random forest classifiers reached accuracies of 97.68%, 95.62%, and 96.5%, respectively. Filtering by anomaly detection and thresholds improved the accuracy to 99.07% (37 samples excluded by filtering), 96.22% (17 samples excluded), and 100% (44 samples excluded) for the neural network, support vector machine and random forest, respectively. Because of best balance between accuracy and number of predictable cases we tested the neural network with applied filters on the in-house cohort, obtaining an accuracy of 95.45%.

INTERPRETATION

We developed a classifier that can differentiate between iCCAs, intrahepatic metastases of a PAAD, and normal bile duct tissue with high accuracy. This tool can be used for improving the diagnosis of pancreato-biliary cancers of the liver.

FUNDING

This work was supported by Berlin Institute of Health (JCS Program), DKTK Berlin (Young Investigator Grant 2022), German Research Foundation (493697503 and 314905040 - SFB/TRR 209 Liver Cancer B01), and German Cancer Aid (70113922).

Proceedings of the 2023 North American Society of Head and Neck Pathology Companion Meeting, New Orleans, LA, March 12, 2023: Navigating New Developments in High Grade Sinonasal Neuroendocrine and Neuroectodermal Neoplasms

Although the definitions of sinonasal neuroendocrine and neuroectodermal neoplasms did not change substantially in the 5th edition WHO Classification of Head and Neck Tumours, the diagnosis of olfactory neuroblastoma (ONB), small cell neuroendocrine carcinoma, and large cell neuroendocrine carcinoma remains quite challenging in practice. Ambiguities surrounding the amount of keratin expression allowable in ONB and the amount of neuroendocrine differentiation seen in sinonasal undifferentiated carcinoma (SNUC) lead to significant diagnostic discrepancies at the high grade end of this tumor spectrum. Furthermore, a group of problematic neuroepithelial tumors that show overlapping features of ONB and neuroendocrine carcinoma have never been recognized in formal classification schemes. Since publication of the 5th edition WHO, two new tumor entities have been proposed that help resolve these problems. Olfactory carcinoma is defined by high grade keratin-positive neuroectodermal cells with frequent intermixed glands and shows recurrent Wnt pathway, ARID1A, and RUNX1 alterations. IDH2-mutant sinonasal carcinoma is a molecularly-defined category that encompasses tumors with undifferentiated (SNUC), large cell neuroendocrine, and neuroepithelial phenotypes. This review will provide a practical overview of these emerging entities and their application to diagnostic challenges in the post-WHO sinonasal neuroendocrine and neuroectodermal tumor classification.

Clinical Behavior, Mutational Profile and T-Cell Repertoire of High-Grade Neuroendocrine Tumors of the Head and Neck

Neuroendocrine carcinomas (NECs) of the head and neck (HN) account for <1% of HN cancers (HNCs), with a 5-year overall survival (OS) <20%. This is a retrospective study of HN NECs diagnosed at our institution between 2005 and 2022. Immunohistochemistry and next-generation sequencing (NGS) were used to evaluate neuroendocrine markers, tumor mutational burden (TMB), mutational profiles and T-cell receptor repertoires. Eleven patients with high-grade HN NECs were identified (male:female ratio 6:5; median age 61 (Min-Max: 31-86)): nasoethmoidal (3), parotid gland (3), submaxillary gland (1), larynx (3) and base of tongue (1). Among n = 8 stage II/IVA/B, all received (chemo)radiotherapy with/without prior surgery or induction chemotherapy, with complete response in 7/8 (87.5%). Among n = 6 recurrent/metastatic patients, three received anti-PD1 (nivolumab (2), pembrolizumab (1)): two achieved partial responses lasting 24 and 10 months. After a median follow-up of 30 and 23.5 months since diagnosis and since recurrent/metastatic, median OS was not reached. Median TMB (n = 7) was 6.72 Mut/Mb. The most common pathogenic variants were TP53, HNF1A, SMARCB1, CDKN2A, PIK3CA, RB1 and MYC. There were 224 median TCR clones (n = 5 pts). In one patient, TCR clones increased from 59 to 1446 after nivolumab. HN NECs may achieve long-lasting survival with multimodality treatment. They harbor moderate-high TMBs and large TCR repertoires, which may explain responses to anti-PD1 agents in two patients and justify the study of immunotherapy in this disease.

Sinonasal Cancer: Improving Classification, Stratification and Therapeutic Options

The nasal cavities and paranasal sinuses are the site of origin of a wide spectrum of histologically and clinically distinct disease entities [...].

A Rare Adult Primary Intracranial Sarcoma, DICER1-Mutant Identified by Epigenomic Profiling: A Case Report

Diagnoses of primary malignant mesenchymal brain tumors are a challenge for pathologists. Here, we report the case of a 52-year-old man with a primary brain tumor, histologically diagnosed as a high-grade glioma, not otherwise specified (NOS). The patient underwent two neurosurgeries in several months, followed by radiotherapy and chemotherapy. We re-examined the tumor samples by methylome profiling. Methylome analysis revealed an epi-signature typical of a primary intracranial sarcoma, DICER1-mutant, an extremely rare tumor. The diagnosis was confirmed by DNA sequencing that revealed a mutation in DICER1 exon 25. DICER1 mutations were not found in the patient's blood cells, thus excluding an inherited DICER1 syndrome. The methylome profile of the DICER1 mutant sarcoma was then compared with that of a high-grade glioma, a morphologically similar tumor type. We found that several relevant regions were differentially methylated. Taken together, we report the morphological, epigenetic, and genetic characterization of the sixth described case of an adult primary intracranial sarcoma, DICER1-mutant to-date. Furthermore, this case report underscores the importance of methylome analysis to refine primary brain tumor diagnosis and to avoid misdiagnosis among morphologically similar subtypes.

Recent advances in epigenetic anticancer therapeutics and future perspectives

Tumor development is frequently accompanied by abnormal expression of multiple genomic genes, which can be broadly viewed as decreased expression of tumor suppressor genes and upregulated expression of oncogenes. In this process, epigenetic regulation plays an essential role in the regulation of gene expression without alteration of DNA or RNA sequence, including DNA methylation, RNA methylation, histone modifications and non-coding RNAs. Therefore, drugs developed for the above epigenetic modulation have entered clinical use or preclinical and clinical research stages, contributing to the development of antitumor drugs greatly. Despite the efficacy of epigenetic drugs in hematologic caners, their therapeutic effects in solid tumors have been less favorable. A growing body of research suggests that epigenetic drugs can be applied in combination with other therapies to increase efficacy and overcome tumor resistance. In this review, the progress of epigenetics in tumor progression and oncology drug development is systematically summarized, as well as its synergy with other oncology therapies. The future directions of epigenetic drug development are described in detail.