The Role of Methylation Analysis in Distinguishing Cellular Myxoma from Low-Grade Myxofibrosarcoma.

Cellular myxoma is a benign soft tissue tumor frequently associated with GNAS mutation that may morphologically resemble low-grade myxofibrosarcoma. This study aimed to identify the undescribed methylation profile of cellular myxoma and compare it to myxofibrosarcoma. We performed molecular analysis on twenty cellular myxomas and nine myxofibrosarcomas and analyzed the results using the methylation-based DKFZ sarcoma classifier. A total of 90% of the cellular myxomas had GNAS mutations (four loci had not been previously described). Copy number variations were found in all myxofibrosarcomas but in none of the cellular myxomas. In the classifier, none of the cellular myxomas reached the 0.9 threshold. Unsupervised t-SNE analysis demonstrated that cellular myxomas form their own clusters, distinct from myxofibrosarcomas. Our study shows the diagnostic potential and the limitations of molecular analysis in cases where morphology and immunohistochemistry are not sufficient to distinguish cellular myxoma from myxofibrosarcoma, particularly regarding GNAS wild-type tumors. The DKFZ sarcoma classifier only provided a valid prediction for one myxofibrosarcoma case; this limitation could be improved by training the tool with a more considerable number of cases. Additionally, the classifier should be introduced to a broader spectrum of mesenchymal neoplasms, including benign tumors like cellular myxoma, whose distinct methylation pattern we demonstrated.

Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling.

The complexity of diagnostic (surgical) pathology has increased substantially over the last decades with respect to histomorphological and molecular profiling. Pathology has steadily expanded its role in tumor diagnostics and beyond from disease entity identification via prognosis estimation to precision therapy prediction. It is therefore not surprising that pathology is among the disciplines in medicine with high expectations in the application of artificial intelligence (AI) or machine learning approaches given their capabilities to analyze complex data in a quantitative and standardized manner to further enhance scope and precision of diagnostics. While an obvious application is the analysis of histological images, recent applications for the analysis of molecular profiling data from different sources and clinical data support the notion that AI will enhance both histopathology and molecular pathology in the future. At the same time, current literature should not be misunderstood in a way that pathologists will likely be replaced by AI applications in the foreseeable future. Although AI will transform pathology in the coming years, recent studies reporting AI algorithms to diagnose cancer or predict certain molecular properties deal with relatively simple diagnostic problems that fall short of the diagnostic complexity pathologists face in clinical routine. Here, we review the pertinent literature of AI methods and their applications to pathology, and put the current achievements and what can be expected in the future in the context of the requirements for research and routine diagnostics.

Salivary carcinosarcoma: insight into multistep pathogenesis indicates uniform origin as sarcomatoid variant of carcinoma ex pleomorphic adenoma with frequent heterologous elements.

AIMS: The formal pathogenesis of salivary carcinosarcoma (SCS) remained unclear, both with respect to the hypothetical development from either preexisting pleomorphic adenoma (PA) or de novo and the clonal relationship between highly heterogeneous carcinomatous and sarcomatous components. METHODS AND RESULTS: We performed clinicopathological and molecular (targeted RNA sequencing) analyses on a large series of 16 cases and combined this with a comprehensive literature search (111 cases). Extensive sampling (average 11.6 blocks), combined with immunohistochemistry and molecular studies (PA-specific translocations including PLAG1 or HMGA2 proven in 6/16 cases), enabled the morphogenetic identification of PA in 15/16 cases (93.8%), by far surpassing a reported rate of 49.6%. Furthermore, we demonstrated a multistep (intraductal/intracapsular/extracapsular) adenoma-carcinoma-sarcoma-progression, based on two alternative histogenetic pathways (intraductal, 56.3%, versus myoepithelial pathway, 37.5%). Thereby, early intracapsular stages are identical to conventional carcinoma ex PA, while later extracapsular stages are dominated by secondary, frequently heterologous sarcomatous transformation with often large tumour size (>60 mm). CONCLUSION: Our findings strongly indicate that SCS (almost) always develops from PA, with a complex multistep adenoma-carcinoma-sarcoma-sequence, based on two alternative histogenetic pathways. The findings from this novel approach strongly suggest that SCS pathogenetically is a rare (3-6%), unique, and aggressive variant of carcinoma ex PA with secondary sarcomatous overgrowth. In analogy to changes of terminology in other organs, the term "sarcomatoid carcinoma ex PA with/without heterologous elements" might be more appropriate.

Machine learning models predict the primary sites of head and neck squamous cell carcinoma metastases based on DNA methylation.

In head and neck squamous cell cancers (HNSCs) that present as metastases with an unknown primary (HNSC-CUPs), the identification of a primary tumor improves therapy options and increases patient survival. However, the currently available diagnostic methods are laborious and do not offer a sufficient detection rate. Predictive machine learning models based on DNA methylation profiles have recently emerged as a promising technique for tumor classification. We applied this technique to HNSC to develop a tool that can improve the diagnostic work-up for HNSC-CUPs. On a reference cohort of 405 primary HNSC samples, we developed four classifiers based on different machine learning models [random forest (RF), neural network (NN), elastic net penalized logistic regression (LOGREG), and support vector machine (SVM)] that predict the primary site of HNSC tumors from their DNA methylation profile. The classifiers achieved high classification accuracies (RF = 83%, NN = 88%, LOGREG = SVM = 89%) on an independent cohort of 64 HNSC metastases. Further, the NN, LOGREG, and SVM models significantly outperformed p16 status as a marker for an origin in the oropharynx. In conclusion, the DNA methylation profiles of HNSC metastases are characteristic for their primary sites, and the classifiers developed in this study, which are made available to the scientific community, can provide valuable information to guide the diagnostic work-up of HNSC-CUP. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Mucosal melanomas of different anatomic sites share a common global DNA methylation profile with cutaneous melanoma but show location-dependent patterns of genetic and epigenetic alterations.

Cutaneous, ocular, and mucosal melanomas are histologically indistinguishable tumors that are driven by a different spectrum of genetic alterations. With current methods, identification of the site of origin of a melanoma metastasis is challenging. DNA methylation profiling has shown promise for the identification of the site of tumor origin in various settings. Here we explore the DNA methylation landscape of melanomas from different sites and analyze if different melanoma origins can be distinguished by their epigenetic profile. We performed DNA methylation analysis, next generation DNA panel sequencing, and copy number analysis of 82 non-cutaneous and 25 cutaneous melanoma samples. We further analyzed eight normal melanocyte cell culture preparations. DNA methylation analysis separated uveal melanomas from melanomas of other primary sites. Mucosal, conjunctival, and cutaneous melanomas shared a common global DNA methylation profile. Still, we observed location-dependent DNA methylation differences in cancer-related genes, such as low frequencies of RARB (7/63) and CDKN2A promoter methylation (6/63) in mucosal melanomas, or a high frequency of APC promoter methylation in conjunctival melanomas (6/9). Furthermore, all investigated melanomas of the paranasal sinus showed loss of PTEN expression (9/9), mainly caused by promoter methylation. This was less frequently seen in melanomas of other sites (24/98). Copy number analysis revealed recurrent amplifications in mucosal melanomas, including chromosomes 4q, 5p, 11q and 12q. Most melanomas of the oral cavity showed gains of chromosome 5p with TERT amplification (8/10), while 11q amplifications were enriched in melanomas of the nasal cavity (7/16). In summary, mucosal, conjunctival, and cutaneous melanomas show a surprisingly similar global DNA methylation profile and identification of the site of origin by DNA methylation testing is likely not feasible. Still, our study demonstrates tumor location-dependent differences of promoter methylation frequencies in specific cancer-related genes together with tumor site-specific enrichment for specific chromosomal changes and genetic mutations. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

The single-cell transcriptional landscape of lung carcinoid tumors.

Lung carcinoid tumors, also referred to as pulmonary neuroendocrine tumors or lung carcinoids, are rare neoplasms of the lung with a more favorable prognosis than other subtypes of lung cancer. Still, some patients suffer from relapsed disease and metastatic spread. Several recent single-cell studies have provided detailed insights into the cellular heterogeneity of more common lung cancers, such as adeno- and squamous cell carcinoma. However, the characteristics of lung carcinoids on the single-cell level are yet completely unknown. To study the cellular composition and single-cell gene expression profiles in lung carcinoids, we applied single-cell RNA sequencing to three lung carcinoid tumor samples and normal lung tissue. The single-cell transcriptomes of carcinoid tumor cells reflected intertumoral heterogeneity associated with clinicopathological features, such as tumor necrosis and proliferation index. The immune microenvironment was specifically enriched in noninflammatory monocyte-derived myeloid cells. Tumor-associated endothelial cells were characterized by distinct gene expression profiles. A spectrum of vascular smooth muscle cells and pericytes predominated the stromal microenvironment. We found a small proportion of myofibroblasts exhibiting features reminiscent of cancer-associated fibroblasts. Stromal and immune cells exhibited potential paracrine interactions which may shape the microenvironment via NOTCH, VEGF, TGFß and JAK/STAT signaling. Moreover, single-cell gene signatures of pericytes and myofibroblasts demonstrated prognostic value in bulk gene expression data. Here, we provide first comprehensive insights into the cellular composition and single-cell gene expression profiles in lung carcinoids, demonstrating the noninflammatory and vessel-rich nature of their tumor microenvironment, and outlining relevant intercellular interactions which could serve as future therapeutic targets.

[Artificial intelligence: a solution for the lack of pathologists?] / Künstliche Intelligenz als Lösung des PathologInnenmangels?

Given the rapid developments, there is no doubt that artificial intelligence (AI) will substantially impact pathological diagnostics. However, it remains an open question if AI will primarily be another diagnostic tool, such as immunohistochemistry, or if AI will also be able to replace human expertise. Most current studies on AI in histopathology deal with relatively simple diagnostic problems and are not yet capable of coping with the complexity of routine diagnostics. While some methods in molecular pathology would already be unthinkable without AI, it remains to be shown how AI will also be able to help with difficult histomorphological differential diagnoses in the future.

Multiclass cancer classification in fresh frozen and formalin-fixed paraffin-embedded tissue by DigiWest multiplex protein analysis.

Histomorphology and immunohistochemistry are the most common ways of cancer classification in routine cancer diagnostics, but often reach their limits in determining the organ origin in metastasis. These cancers of unknown primary, which are mostly adenocarcinomas or squamous cell carcinomas, therefore require more sophisticated methodologies of classification. Here, we report a multiplex protein profiling-based approach for the classification of fresh frozen and formalin-fixed paraffin-embedded (FFPE) cancer tissue samples using the digital western blot technique DigiWest. A DigiWest-compatible FFPE extraction protocol was developed, and a total of 634 antibodies were tested in an initial set of 16 FFPE samples covering tumors from different origins. Of the 303 detected antibodies, 102 yielded significant correlation of signals in 25 pairs of fresh frozen and FFPE primary tumor samples, including head and neck squamous cell carcinomas (HNSC), lung squamous cell carcinomas (LUSC), lung adenocarcinomas (LUAD), colorectal adenocarcinomas (COAD), and pancreatic adenocarcinomas (PAAD). For this signature of 102 analytes (covering 88 total proteins and 14 phosphoproteins), a support vector machine (SVM) algorithm was developed. This allowed for the classification of the tissue of origin for all five tumor types studied here with high overall accuracies in both fresh frozen (90.4%) and FFPE (77.6%) samples. In addition, the SVM classifier reached an overall accuracy of 88% in an independent validation cohort of 25 FFPE tumor samples. Our results indicate that DigiWest-based protein profiling represents a valuable method for cancer classification, yielding conclusive and decisive data not only from fresh frozen specimens but also FFPE samples, thus making this approach attractive for routine clinical applications.

[Quality assurance in diagnostic in situ hybridization-experience of QuIP]. / Qualitätssicherung in der diagnostischen In-situ-Hybridisierung ­ Erfahrungen der QuIP.

The Quality Assurance Initiative Pathology (QuIP) gives pathologists the opportunity to check the methodological processes of immunohistological and molecular diagnostics in a result-oriented manner and obtain a certificate reflecting the quality. For in situ hybridization (ISH), 5 round robin tests were organized in 2019, two recurrent (HER2-ISH gastric carcinomas and HER2-ISH breast carcinomas) and three prototypical (ROS1-NSCLC, ALK1-NSCLC, NTRK). The different round robin tests, which were provided by QuIP, are based on the development in diagnostics and the importance of the therapeutic relevance of the molecules which are tested. The results of the round robin tests in 2019 showed a sensitivity of at least 94.4%, a specificity of at least 96.6%, and a success rate of 85-99%. This reflected the high standard of quality of the round robin test and the participating institutes.

DNA methylation profiling reliably distinguishes pulmonary enteric adenocarcinoma from metastatic colorectal cancer.

Pulmonary enteric adenocarcinoma is a rare non-small cell lung cancer subtype. It is poorly characterized and cannot be distinguished from metastatic colorectal or upper gastrointestinal adenocarcinomas by means of routine pathological methods. As DNA methylation patterns are known to be highly tissue specific, we aimed to develop a methylation-based algorithm to differentiate these entities. To this end, genome-wide methylation profiles of 600 primary pulmonary, colorectal, and upper gastrointestinal adenocarcinomas obtained from The Cancer Genome Atlas and the Gene Expression Omnibus database were used as a reference cohort to train a machine learning algorithm. The resulting classifier correctly classified all samples from a validation cohort consisting of 680 primary pulmonary, colorectal and upper gastrointestinal adenocarcinomas, demonstrating the ability of the algorithm to reliably distinguish these three entities. We then analyzed methylation data of 15 pulmonary enteric adenocarcinomas as well as four pulmonary metastases and four primary colorectal adenocarcinomas with the algorithm. All 15 pulmonary enteric adenocarcinomas were reliably classified as primary pulmonary tumors and all four metastases as well as all four primary colorectal cancer samples were identified as colorectal adenocarcinomas. In a t-distributed stochastic neighbor embedding analysis, the pulmonary enteric adenocarcinoma samples did not form a separate methylation subclass but rather diffusely intermixed with other pulmonary cancers. Additional characterization of the pulmonary enteric adenocarcinoma series using fluorescence in situ hybridization, next-generation sequencing and copy number analysis revealed KRAS mutations in nine of 15 samples (60%) and a high number of structural chromosomal changes. Except for an unusually high rate of chromosome 20 gain (67%), the molecular data was mostly reminiscent of standard pulmonary adenocarcinomas. In conclusion, we provide sound evidence of the pulmonary origin of pulmonary enteric adenocarcinomas and in addition provide a publicly available machine learning-based algorithm to reliably distinguish these tumors from metastatic colorectal cancer.

Assessment of the extracellular volume fraction for the grading of clear cell renal cell carcinoma: first results and histopathological findings.

OBJECTIVES: To assess the potential of T1 mapping-based extracellular volume fraction (ECV) for the identification of higher grade clear cell renal cell carcinoma (cRCC), based on histopathology as the reference standard. METHODS: For this single-center, institutional review board-approved prospective study, 27 patients (17 men, median age 62 ± 12.4 years) with pathologic diagnosis of cRCC (nucleolar International Society of Urological Pathology (ISUP) grading) received abdominal MRI scans at 1.5 T using a modified Look-Locker inversion recovery (MOLLI) sequence between January 2017 and June 2018. Quantitative T1 values were measured at different time points (pre- and postcontrast agent administration) and quantification of the ECV was performed on MRI and histological sections (H&E staining). RESULTS: Reduction in T1 value after contrast agent administration and MR-derived ECV were reliable predictors for differentiating higher from lower grade cRCC. Postcontrast T1diff values (T1diff = T1 difference between the native and nephrogenic phase) and MR-derived ECV were significantly higher for higher grade cRCC (ISUP grades 3-4) compared with lower grade cRCC (ISUP grades 1-2) (p < 0.001). A cutoff value of 700 ms could distinguish higher grade from lower grade tumors with 100% (95% CI 0.69-1.00) sensitivity and 82% (95% CI 0.57-0.96) specificity. There was a positive and strong correlation between MR-derived ECV and histological ECV (p < 0.01, r = 0.88). Interobserver agreement for quantitative longitudinal relaxation times in the T1 maps was excellent. CONCLUSIONS: T1 mapping with ECV measurement could represent a novel in vivo biomarker for the classification of cRCC regarding their nucleolar grade, providing incremental diagnostic value as a quantitative MR marker. KEY POINTS: • Reduction in MRI T1 relaxation times after contrast agent administration and MR-derived extracellular volume fraction are useful parameters for grading of clear cell renal cell carcinoma (cRCC). • T1 differences between the native and the nephrogenic phase are higher for higher grade cRCC compared with lower grade cRCC and MRI-derived extracellular volume fraction (ECV) and histological ECV show a strong correlation. • T1 mapping with ECV measurement may be helpful for the noninvasive assessment of cRCC pathology, being a safe and feasible method, and it has potential to optimize individualized treatment options, e.g., in the decision of active surveillance.

Immunohistochemical analysis of Bcl-2, nuclear S100A4, MITF and Ki67 for risk stratification of early-stage melanoma - A combined IHC score for melanoma risk stratification.

BACKGROUND AND OBJECTIVES: Overall survival (OS) in patients with early-stage malignant melanoma differs. To date, there are no established prognostic markers. We aimed to contribute to a better understanding of potential prognostic immunohistochemical markers for risk stratification. PATIENTS AND METHODS: 161 surgically resected early-stage malignant melanomas (stage pT1 and pT2) were analyzed for expression of 20 different proteins using immunohistochemistry. The results were correlated with OS. The cohort was randomly split into a discovery and a validation cohort. RESULTS: High Bcl-2 expression, high nuclear S100A4 expression as well as a Ki67 proliferation index of ≥ 20 % were associated with shorter OS. Strong MITF immunoreactivity was a predictor for favorable prognosis. A combination of these four markers resulted in a multi-marker score with significant prognostic value in multivariate survival analysis (HR: 3.704; 95 % CI 1.484 to 9.246; p = 0.005). Furthermore, the score was able to differentiate a low-risk group with excellent OS rates (five-year survival rate: 100 %), an intermediate-risk group (five-year survival rate: 81.8 %) and a high-risk group (five-year survival rate: 52.6 %). The prognostic value was confirmed within the validation cohort. CONCLUSIONS: Combined immunohistochemical analysis of Bcl-2, nuclear S100A4, Ki67 and MITF could contribute to better risk stratification of early-stage malignant melanoma patients.

RNA-based analysis of ALK fusions in non-small cell lung cancer cases showing IHC/FISH discordance.

BACKGROUND: Rearrangements of the anaplastic lymphoma kinase (ALK) belong to the promising targets in the therapy of advanced non-small cell lung cancer (NSCLC) and are predominantly detected by immunohistochemistry (IHC) and/or fluorescence in-situ hybridization (FISH). However, both methods occasionally produce discordant results, especially in so-called borderline (BL) cases, showing ALK FISH-positive signals in 10-20% of the tumor nuclei around the cutoff (15%). This leads to a diagnostic and thus to a therapeutic dilemma. METHODS: We selected 18 unequivocal (12 ALK IHC/FISH-negative; 6 ALK IHC/FISH-positive) and 15 equivocal samples with discordant results between FISH (Abbott, Vysis LSI ALK Dual Color) and IHC (Ventana, D5F3), including cases with FISH-BL results, for further RNA based-analysis. To detect ALK rearrangement at the transcriptional level, RNA was analyzed using a targeted multiplex-PCR panel followed by IonTorrent sequencing and by direct transcript counting using a digital probe-based assay (NanoString). Sensitivity of both methods was defined using RNA obtained from an ALK-positive cell line dilution series. RESULTS: Cases with unequivocal IHC/FISH results showed concordant data with both RNA-based methods, whereas the three IHC-negative/FISH-positive samples were negative. The four IHC-negative/FISH-BL-negative cases, as well as the five IHC-negative/FISH-BL-positive samples showed negative results by massive parallel sequencing (MPS) and digital probe-based assay. The two IHC-positive/FISH-BL-positive cases were both positive on the RNA-level, whereas a tumor with questionable IHC and FISH-BL-positive status displayed no ALK fusion transcript. CONCLUSIONS: The comparison of methods for the confirmation of ALK rearrangements revealed that the detection of ALK protein by IHC and ALK fusion transcripts on transcriptional level by MPS and the probe-based assay leads to concordant results. Only a small proportion of clearly ALK FISH-positive cases are unable to express the ALK protein and ALK fusion transcript which might explain a non-responding to ALK inhibitors. Therefore, our findings led us to conclude that ALK testing should initially be based on IHC and/or RNA-based methods.

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.

[Explainable artificial intelligence in pathology]. / Erklärbare Künstliche Intelligenz in der Pathologie.

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).

Genome-Wide DNA Methylation Profiling as a Prognostic Marker in Pituitary Adenomas-A Pilot Study.

BACKGROUND: The prediction of the regrowth potential of pituitary adenomas after surgery is challenging. The genome-wide DNA methylation profiling of pituitary adenomas may separate adenomas into distinct methylation classes corresponding to histology-based subtypes. Specific genes and differentially methylated probes involving regrowth have been proposed, but no study has linked this epigenetic variance with regrowth potential and the clinical heterogeneity of nonfunctioning pituitary adenomas. This study aimed to investigate whether DNA methylation profiling can be useful as a clinical prognostic marker. METHODS: A DNA methylation analysis by Illumina's MethylationEPIC array was performed on 54 pituitary macroadenomas from patients who underwent transsphenoidal surgery during 2007-2017. Twelve patients were excluded due to an incomplete postoperative follow-up, degenerated biobank-stored tissue, or low DNA methylation quality. For the quantitative measurement of the tumor regrowth rate, we conducted a 3D volumetric analysis of tumor remnant volume via annual magnetic resonance imaging. A linear mixed effects model was used to examine whether different DNA methylation clusters had different regrowth patterns. RESULTS: The DNA methylation profiling of 42 tissue samples showed robust DNA methylation clusters, comparable with previous findings. The subgroup of 33 nonfunctioning pituitary adenomas of an SF1-lineage showed five subclusters with an approximately unbiased score of 86%. There were no overall statistically significant differences when comparing hazard ratios for regrowth of 100%, 50%, or 0%. Despite this, plots of correlated survival estimates suggested higher regrowth rates for some clusters. The mixed effects model of accumulated regrowth similarly showed tendencies toward an association between specific DNA methylation clusters and regrowth potential. CONCLUSION: The DNA methylation profiling of nonfunctioning pituitary adenomas may potentially identify adenomas with increased growth and recurrence potential. Larger validation studies are needed to confirm the findings from this explorative pilot study.

High-dimensional in situ proteomics imaging to assess γδ T cells in spatial biology.

This study presents a high-dimensional immunohistochemistry approach to assess human γδ T cell subsets in their native tissue microenvironments at spatial resolution, a hitherto unmet scientific goal due to the lack of established antibodies and required technology. We report an integrated approach based on multiplexed imaging and bioinformatic analysis to identify γδ T cells, characterize their phenotypes, and analyze the composition of their microenvironment. Twenty-eight γδ T cell microenvironments were identified in tissue samples from fresh frozen human colon and colorectal cancer where interaction partners of the immune system, but also cancer cells were discovered in close proximity to γδ T cells, visualizing their potential contributions to cancer immunosurveillance. While this proof-of-principle study demonstrates the potential of this cutting-edge technology to assess γδ T cell heterogeneity and to investigate their microenvironment, future comprehensive studies are warranted to associate phenotypes and microenvironment profiles with features such as relevant clinical characteristics.

Proteomic profiling reveals ACSS2 facilitating metabolic support in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease characterized by genomic aberrations in oncogenes, cytogenetic abnormalities, and an aberrant epigenetic landscape. Nearly 50% of AML cases will relapse with current treatment. A major source of therapy resistance is the interaction of mesenchymal stroma with leukemic cells resulting in therapeutic protection. We aimed to determine pro-survival/anti-apoptotic protein networks involved in the stroma protection of leukemic cells. Proteomic profiling of cultured primary AML (n = 14) with Hs5 stroma cell line uncovered an up-regulation of energy-favorable metabolic proteins. Next, we modulated stroma-induced drug resistance with an epigenetic drug library, resulting in reduced apoptosis with histone deacetylase inhibitor (HDACi) treatment versus other epigenetic modifying compounds. Quantitative phosphoproteomic probing of this effect further revealed a metabolic-enriched phosphoproteome including significant up-regulation of acetyl-coenzyme A synthetase (ACSS2, S30) in leukemia-stroma HDACi treated cocultures compared with untreated monocultures. Validating these findings, we show ACSS2 substrate, acetate, promotes leukemic proliferation, ACSS2 knockout in leukemia cells inhibits leukemic proliferation and ACSS2 knockout in the stroma impairs leukemic metabolic fitness. Finally, we identify ACSS1/ACSS2-high expression AML subtype correlating with poor overall survival. Collectively, this study uncovers the leukemia-stroma phosphoproteome emphasizing a role for ACSS2 in mediating AML growth and drug resistance.

[New information about tumours of the salivary glands : WHO classification 2022]. / Neues zu Tumoren der Speicheldrüsen : WHO-Klassifikation 2022.

The WHO 2022 classification of head and neck tumours contains another slight increase in the number of listed benign and malignant tumour entities of the salivary glands. This includes conceptual changes and alterations in the terminology of some entities. While some new features are regarded as preliminary or provisional, others are strongly disputed (for example the terminology of intraductal carcinoma). The impact of molecular findings, mainly recurrent gene fusions, continues to increase rapidly and some have been included in the definition of certain tumour entities. The significance of molecular findings is, however, still largely restricted to diagnostic aspects. Newly included entities include microsecretory carcinoma (defined by an SS18::MEF2C fusion), sclerosing microcystic adenocarcinoma (similar to skin adnexal tumours of the same name) and mucinous adenocarcinoma (characterized by AKT1 mutations with heterogeneous morphology).