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.

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.

Somatic genome alterations in relation to age in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common cause of global cancer-related mortality and the major risk factor is smoking consumption. By analyzing 486 LUAD samples from The Cancer Genome Atlas, we detected a higher mutational burden among younger patients in the global cohort as well as in the TP53-mutated subcohort. The interaction effect of patient age and TP53 mutations significantly affected the mutational rate of younger TP53-mutated patients. Furthermore, we detected a significant enrichment of the smoking-related signature SI4 (SI4) among younger TP53-mutated patients, meanwhile the age-related Signature 1 (SI1) significantly increased in proportion to patient age. Although present and past smoking is reported in the TP53 wild-type patients, we observed a lower average number of somatic mutations, with no correlation with patient age. Overall, TP53 mutations were significantly higher in younger patients and mainly characterized by SI4 and Signature 24 (SI24). Therefore, TP53 seemed to acquire a particular sensitivity to smoking related C>A mutations in younger patients. We hypothesize that TP53 mutations at a younger age might be a crucial factor enhancing the sensitivity to smoking-related mutations leading to a burst of somatic alterations. The mutational profile of cancer cell might reflect the mutational processes operative in aging in a given tissue. Therefore, TP53-mutated and TP53 wild-type patient groups might represent phenotypes which endure aging-related mutational processes with different strength. Our study provides indications of age-dependent differences in mutational backgrounds that might be relevant for cancer prevention and age-adjusted treatment approaches.

Proteogenomic systems analysis identifies targeted therapy resistance mechanisms in EGFR-mutated lung cancer.

Cancer precision medicine largely relies on knowledge about genetic aberrations in tumors and next-generation-sequencing studies have shown a high mutational complexity in many cancers. Although a large number of the observed mutations is believed to be not causally linked with cancer, the functional effects of many rare mutations but also of combinations of driver mutations are often unknown. Here, we perform a systems analysis of a model of EGFR-mutated nonsmall cell lung cancer resistant to targeted therapy that integrates whole exome sequencing, global time-course discovery phosphoproteomics and computational modeling to identify functionally relevant molecular alterations. Our approach allows for a complexity reduction from over 2,000 genetic events potentially involved in mediating resistance to only 44 phosphoproteins and 35 topologically close genetic alterations. We perform single- and combination-drug testing against the predicted phosphoproteins and discovered that targeting of HSPB1, DBNL and AKT1 showed potent antiproliferative effects overcoming resistance against EGFR-inhibitory therapy. Our approach may therefore be used to complement mutational profiling to identify functionally relevant molecular aberrations and propose combination therapies across cancers.

Histomorphological and molecular profiling: friends not foes! Morpho-molecular analysis reveals agreement between histological and molecular profiling.

AIMS: Whereas current cancer diagnosis largely relies on the well-established organ and tissue typing of tumours, partially complemented by molecular properties, the comprehensive molecular profiling efforts of recent years have stimulated proposals for molecular reclassifications of tumours independently of anatomical origin. Proposals based only on mutational profiles show the least concordance with histotypes, whereas greater concordance is achieved when various genomic and proteomic data are included. METHODS AND RESULTS: The most comprehensive molecular reclassification of tumours, by Hoadley et  al (Cell, 158, 2014; 929) and Hoadley et  al (Cell, 173, 2018; 291), integrated multi-omics data, and proposes novel molecular tumour classes. To investigate the relationship between the proposed molecular classes and the original histological tumour types, we re-examined the histomorphology of molecularly reclassified cases. Our results show that the claimed molecular reclassification is associated with and explainable by specific histological subtypes in 70% of the reclassified cases. CONCLUSION: Therefore, in contrast to the proclaimed reclassification and independence of molecular and histological tumour types, our analysis demonstrates that comprehensive molecular profiling, which includes gene expression and methylation as well as proteomic profiling in addition to mutational analyses, is largely consistent with histomorphological tumour properties.

Copy number changes of clinically actionable genes in melanoma, non-small cell lung cancer and colorectal cancer-A survey across 822 routine diagnostic cases.

Targeted deep massive parallel sequencing has been implemented in routine molecular diagnostics for high-throughput genetic profiling of formalin-fixed paraffin-embedded (FFPE) cancer samples. This approach is widely used to interrogate simple somatic mutations but experience with the analysis of copy number variations (CNV) is limited. Here, we retrospectively analyzed CNV in 822 cancer cases (135 melanoma, 468 non-small cell lung cancers (NSCLC), 219 colorectal cancers (CRC)). We observed a decreasing frequency of CNV in clinically actionable genes from melanoma to NSCLC to CRC. The overall cohort displayed 168 (20%) amplifications in 17 druggable targets. The majority of BRAF mutant melanomas (54%) showed co-occurring CNV in other genes, mainly affecting CDKN2A. Subsets showed clustered deletions in ABL1, NOTCH1, RET or STK11, GNA11, and JAK3. Most NRAS mutant melanomas (49%) harbored CNVs in other genes with CDKN2A and FGFR3 being most frequently affected. Five BRAF/NRASwt tumors had co-amplifications of KDR, KIT, PDGFRA and another six mutated KIT. Among all NSCLC, we identified 14 EGFRamp (with ten EGFRmut) and eight KRASamp (with seven KRASmut). KRASmut tumors displayed frequent amplifications of MYC (n = 10) and MDM2 (n = 5). Fifteen KRAS/EGFR/BRAFwt tumors had MET mutations/amplifications. In CRC, amplified IGF2 was most prevalent (n = 13) followed by MYC (n = 9). Two cases showed amplified KRAS wildtype alleles. Two of the KRASmut cases harbored amplifications of NRAS and three KRASwt cases amplification of EGFR. In conclusion, we demonstrate that our approach i) facilitates detection of CNV, ii) enables detection of known CNV patterns, and iii) uncovers new CNV of clinically actionable genes in FFPE tissue samples across cancers. © 2016 Wiley Periodicals, Inc.

Reduction in Powder Wall Friction by an a-C:H:Si Film.

The wall friction angle is an important parameter in powder flow. In a recent study for various powders, a reduction in the wall friction angle for steel was demonstrated by the application of an a-C:H:Si film on the steel surface. This work presents the results of a study of this effect in more detail regarding the influence of the powder material, the wall normal stress and the particle size of the powder for mass median diameters from 4 µm to approximately 150 µm. The wall friction angles were measured using a Schulze ring shear tester for three different powder materials: aluminum oxide, calcium carbonate and silicon carbide. The results showed little difference with respect to powder chemistry. For the coarser powders, the reduction in the wall friction angle due to the a-C:H:Si coating was highest (10° to 12°) and rather stress-independent, while for the fine and medium-size powders the reduction was lower and stress-dependent. With increasing wall normal stress, the reduction in the wall friction angle increased. These results can be explained by the friction reduction mechanism of a-C:H:Si, which requires a certain contact pressure for superficial graphitization.

Music therapy to promote movement from isolation to community in homeless veterans.

The U.S. Department of Veterans Affairs' Operation Stand Down has done much to address homeless needs among veterans. Gaining client trust is central to the effectiveness of the program. Music therapy has been found beneficial in moving individuals from isolation to community. We report our experience with participatory music therapy in Operation Stand Down and offer this as a legitimate intervention to enhance client participation.

Patient-level proteomic network prediction by explainable artificial intelligence.

Understanding the pathological properties of dysregulated protein networks in individual patients' tumors is the basis for precision therapy. Functional experiments are commonly used, but cover only parts of the oncogenic signaling networks, whereas methods that reconstruct networks from omics data usually only predict average network features across tumors. Here, we show that the explainable AI method layer-wise relevance propagation (LRP) can infer protein interaction networks for individual patients from proteomic profiling data. LRP reconstructs average and individual interaction networks with an AUC of 0.99 and 0.93, respectively, and outperforms state-of-the-art network prediction methods for individual tumors. Using data from The Cancer Proteome Atlas, we identify known and potentially novel oncogenic network features, among which some are cancer-type specific and show only minor variation among patients, while others are present across certain tumor types but differ among individual patients. Our approach may therefore support predictive diagnostics in precision oncology by inferring "patient-level" oncogenic mechanisms.

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.

Surface-assisted assembly of discrete porphyrin-based cyclic supramolecules.

We employed de novo synthesized porphyrin modules to construct discrete cyclic supramolecular architectures supported on a copper surface. The programmed geometry and functionality of the molecular modules together with their conformational flexibility and substrate interaction yields symmetric discrete assemblies, including dimers and chains as well as three- to six-membered cyclic structures. The area of the molecular cavities is extended by creating bicomponent structures combining building blocks with different symmetry.

Self-assembly of flexible one-dimensional coordination polymers on metal surfaces.

We employed a de novo synthesized porphyrin module to construct one-dimensional (1D) Cu-coordinated polymers on Cu(111) and Ag(111) surfaces. The programmed geometry and functionality of the molecular module together with its conformational flexibility and substrate interaction yields sinuous metal-organic polymeric assemblies, based on an unusual two-fold Cu-pyridyl coordination motif. An analysis of scanning tunneling microscopy (STM) data reveals the occurrence of two enantiomers, resulting from the surface confinement that deconvolutes the module in 2D-chiral conformational isomers. The stereoisomers exhibit site-specific surface anchoring, from whence three discrete orientations are possible for each species. Their sequence and mutual arrangement determine direction and curvature of the metal-organic chains. The Cu-coordinated polymers are very similar on both Cu(111) and Ag(111), where their formation is induced by intrinsic and coevaporated adatoms, respectively, which indicates that the lateral bonding motif is predominantly independent of the substrate. In addition, molecular manipulation experiments show the collective motion of entire segments of the Cu-coordinated multi-porphyrin polymers.

Tissue clonality of dendritic cell subsets and emergency DCpoiesis revealed by multicolor fate mapping of DC progenitors.

Conventional dendritic cells (cDCs) are found in all tissues and play a key role in immune surveillance. They comprise two major subsets, cDC1 and cDC2, both derived from circulating precursors of cDCs (pre-cDCs), which exited the bone marrow. We show that, in the steady-state mouse, pre-cDCs entering tissues proliferate to give rise to differentiated cDCs, which themselves have residual proliferative capacity. We use multicolor fate mapping of cDC progenitors to show that this results in clones of sister cDCs, most of which comprise a single cDC1 or cDC2 subtype, suggestive of pre-cDC commitment. Upon infection, a surge in the influx of pre-cDCs into the affected tissue dilutes clones and increases cDC numbers. Our results indicate that tissue cDCs can be organized in a patchwork of closely positioned sister cells of the same subset whose coexistence is perturbed by local infection, when the bone marrow provides additional pre-cDCs to meet increased tissue demand.

Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers.

BACKGROUND: Comprehensive mutational profiling data now available on all major cancers have led to proposals of novel molecular tumor classifications that modify or replace the established organ- and tissue-based tumor typing. The rationale behind such molecular reclassifications is that genetic alterations underlying cancer pathology predict response to therapy and may therefore offer a more precise view on cancer than histology. The use of individual actionable mutations to select cancers for treatment across histotypes is already being tested in the so-called basket trials with variable success rates. Here, we present a computational approach that facilitates the systematic analysis of the histological context dependency of mutational effects by integrating genomic and proteomic tumor profiles across cancers. METHODS: To determine effects of oncogenic mutations on protein profiles, we used the energy distance, which compares the Euclidean distances of protein profiles in tumors with an oncogenic mutation (inner distance) to that in tumors without the mutation (outer distance) and performed Monte Carlo simulations for the significance analysis. Finally, the proteins were ranked by their contribution to profile differences to identify proteins characteristic of oncogenic mutation effects across cancers. RESULTS: We apply our approach to four current proposals of molecular tumor classifications and major therapeutically relevant actionable genes. All 12 actionable genes evaluated show effects on the protein level in the corresponding tumor type and showed additional mutation-related protein profiles in 21 tumor types. Moreover, our analysis identifies consistent cross-cancer effects for 4 genes (FGFR1, ERRB2, IDH1, KRAS/NRAS) in 14 tumor types. We further use cell line drug response data to validate our findings. CONCLUSIONS: This computational approach can be used to identify mutational signatures that have protein-level effects and can therefore contribute to preclinical in silico tests of the efficacy of molecular classifications as well as the druggability of individual mutations. It thus supports the identification of novel targeted therapies effective across cancers and guides efficient basket trial designs.

Somatic genome alterations in relation to age in lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is the most common cause of global cancer-related mortality and the major risk factors is smoking consumption. By analyzing ∼500 LUSC samples from The Cancer Genome Atlas, we detected a higher mutational burden as well as a higher level of methylation changes in younger patients. The SNPs mutational profiling showed enrichments of smoking-related signature 4 and defective DNA mismatch repair (MMR)-related signature 6 in younger patients, while the defective DNA MMR signature 26 was enriched among older patients. Furthermore, gene set enrichment analysis was performed in order to explore functional effect of somatic alterations in relation to patient age. Extracellular Matrix-Receptor Interaction, Nucleotide Excision Repair and Axon Guidance seem crucial disrupted pathways in younger patients. We hypothesize that a higher sensitivity to smoking-related damages and the enrichment of defective DNA MMR related mutations may contribute to the higher mutational burden of younger patients. The two distinct age-related defective DNA MMR signatures 6 and 26 might be crucial mutational patterns in LUSC tumorigenesis which may develop distinct phenotypes. Our study provides indications of age-dependent differences in mutational backgrounds (SNPs and CNVs) as well as epigenetic patterns that might be relevant for age adjusted treatment approaches.

DNA copy number changes define spatial patterns of heterogeneity in colorectal cancer.

Genetic heterogeneity between and within tumours is a major factor determining cancer progression and therapy response. Here we examined DNA sequence and DNA copy-number heterogeneity in colorectal cancer (CRC) by targeted high-depth sequencing of 100 most frequently altered genes. In 97 samples, with primary tumours and matched metastases from 27 patients, we observe inter-tumour concordance for coding mutations; in contrast, gene copy numbers are highly discordant between primary tumours and metastases as validated by fluorescent in situ hybridization. To further investigate intra-tumour heterogeneity, we dissected a single tumour into 68 spatially defined samples and sequenced them separately. We identify evenly distributed coding mutations in APC and TP53 in all tumour areas, yet highly variable gene copy numbers in numerous genes. 3D morpho-molecular reconstruction reveals two clusters with divergent copy number aberrations along the proximal-distal axis indicating that DNA copy number variations are a major source of tumour heterogeneity in CRC.

Histological tumor typing in the age of molecular profiling.

Clinical oncology and pathological diagnostics regard cancer as an organ- and tissue-specific disease. Comprehensive mutational profiling information from next-generation sequencing projects may be used to study to what extent the anatomic tumor classifications relate to the observed molecular profiles. Here, we review data that show substantial genetic similarities across major anatomic cancer types and that propose novel tumor classifications based on mutational profiling. Although these studies provide important insight into molecular tumor properties and some even propose novel tumor classification systems, current clinical evidence is lacking that genetic tumor profiling is sufficient to replace histological tumor typing. Recent studies rather show that targeted treatments efficaceous in one tumor type are not necessarily successful in another despite the presence of the same (actionable) mutations. We discuss the implications of the observed complex mutational tumor profiles for targeted therapy selection and future trial design in precision oncology.

CognitionMaster: an object-based image analysis framework.

BACKGROUND: Automated image analysis methods are becoming more and more important to extract and quantify image features in microscopy-based biomedical studies and several commercial or open-source tools are available. However, most of the approaches rely on pixel-wise operations, a concept that has limitations when high-level object features and relationships between objects are studied and if user-interactivity on the object-level is desired. RESULTS: In this paper we present an open-source software that facilitates the analysis of content features and object relationships by using objects as basic processing unit instead of individual pixels. Our approach enables also users without programming knowledge to compose "analysis pipelines" that exploit the object-level approach. We demonstrate the design and use of example pipelines for the immunohistochemistry-based cell proliferation quantification in breast cancer and two-photon fluorescence microscopy data about bone-osteoclast interaction, which underline the advantages of the object-based concept. CONCLUSIONS: We introduce an open source software system that offers object-based image analysis. The object-based concept allows for a straight-forward development of object-related interactive or fully automated image analysis solutions. The presented software may therefore serve as a basis for various applications in the field of digital image analysis.

Hemocompatibility of Inorganic Physical Vapor Deposition (PVD) Coatings on Thermoplastic Polyurethane Polymers.

Biocompatibility improvements for blood contacting materials are of increasing interest for implanted devices and interventional tools. The current study focuses on inorganic (titanium, titanium nitride, titanium oxide) as well as diamond-like carbon (DLC) coating materials on polymer surfaces (thermoplastic polyurethane), deposited by magnetron sputtering und pulsed laser deposition at room temperature. DLC was used pure (a-C:H) as well as doped with silicon, titanium, and nitrogen + titanium (a-C:H:Si, a-C:H:Ti, a-C:H:N:Ti). In-vitro testing of the hemocompatibility requires mandatory dynamic test conditions to simulate in-vivo conditions, e.g., realized by a cone-and-plate analyzer. In such tests, titanium- and nitrogen-doped DLC and titanium nitride were found to be optimally anti-thrombotic and better than state-of-the-art polyurethane polymers. This is mainly due to the low tendency to platelet microparticle formation, a high content of remaining platelets in the whole blood after testing and low concentration of platelet activation and aggregation markers. Comparing this result to shear-flow induced cell motility tests with e.g., Dictostelium discoideum cell model organism reveals similar tendencies for the investigated materials.

Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach.

Automated image analysis of cells and tissues has been an active research field in medical informatics for decades but has recently attracted increased attention due to developments in computer and microscopy hardware and the awareness that scientific and diagnostic pathology require novel approaches to perform objective quantitative analyses of cellular and tissue specimens. Model-based approaches use a priori information on cell shape features to obtain the segmentation, which may introduce a bias favouring the detection of cell nuclei only with certain properties. In this study we present a novel contour-based "minimum-model" cell detection and segmentation approach that uses minimal a priori information and detects contours independent of their shape. This approach avoids a segmentation bias with respect to shape features and allows for an accurate segmentation (precision = 0.908; recall = 0.859; validation based on ∼8000 manually-labeled cells) of a broad spectrum of normal and disease-related morphological features without the requirement of prior training.