Cancer evolves from a combination of genetic and epigenetic abnormalities resulting in aberrant gene expression profiles as well as altered epigenomic patterns. Epigenetic alterations such as DNA methylation and histone modification play an important role in tumorigenesis. While in the pathobiology of uveal melanoma (UM) genetic changes have been well characterized, there is growing evidence suggesting that epigenetic changes are also involved. We investigated whether epigenetic modifications (global levels of histone acetylation, DNA methylation, ubiquitination) are detectable in UM tissues compared to healthy controls with respect to inter- and intratumoral heterogeneity. Formalin-fixed paraffin-embedded tissues of primary UM (n = 15), UM metastasis (n = 13), and control choroid (n = 12) were immunohistochemically investigated by two ophthalmic pathologists for global levels of histone acetylation (Histone 3 acetylation, H3Ac; Histone 4 acetylation, H4Ac), DNA methylation (5-methylcytosine, 5-MeC; 5'-hydroxymethylcytosine, 5-hMeC), global ubiquitination (UBC) as well as Ubiquityl-Histone H2A (H2Aub). The nuclear staining intensity of primary tumors, metastases and control choroids was evaluated using a score from 0 to 3, which was multiplied with the percentage of stained cells (score from 0 to 4). The control choroid and the choroid next to the tumor showed a more intense nuclear staining than the primary tumor tissue. The choroid next to the tumor was stained less than the control choroid. The nuclear staining intensity in the tumor was comparable to that in the metastases. The tumor tissue itself often exhibited a heterogeneous staining pattern, as nuclei in the tumor center were less intensely stained than in the periphery. Cells with a presumed invasive potential (extraocular extension, growth along emissary canals) showed also an intense staining reaction. Although no prognostically relevant pattern of global epigentic markers could be identified, our results suggest that epigenetic changes play a role in UM pathogenesis and metastasis. In particular the staining reaction of tumor cell subtypes with a presumed invasive potential warrants further attention. The role of epigenetically relevant interactions with the tumor micromilieu should be further investigated as immune cells are predominantly located in the tumor periphery which showed a different staining intensity than the tumor center. However, as considerable epigenetic diversity exists in primary tumors, studies on biopsy tissue are not recommended for the immunohistochemical investigation of epigenetic markers.

Rationale: Pancreatic ductal adenocarcinoma (PDAC) remains a tumor entity of exceptionally poor prognosis, and several biomarkers are under current investigation for the prediction of patient prognosis. Many studies focus on promoting newly developed imaging biomarkers without a rigorous comparison to other established parameters. To assess the true value and leverage the potential of all efforts in this field, a multi-parametric evaluation of the available biomarkers for PDAC survival prediction is warranted. Here we present a multiparametric analysis to assess the predictive value of established parameters and the added contribution of newly developed imaging features such as biomarkers for overall PDAC patient survival. Methods: 103 patients with resectable PDAC were retrospectively enrolled. Clinical and histopathological data (age, sex, chemotherapy regimens, tumor size, lymph node status, grading and resection status), morpho-molecular and genetic data (tumor morphology, molecular subtype, tp53, kras, smad4 and p16 genetics), image-derived features and the combination of all parameters were tested for their prognostic strength based on the concordance index (CI) of multivariate Cox proportional hazards survival modelling after unsupervised machine learning preprocessing. Results: The average CIs of the out-of-sample data were: 0.63 for the clinical and histopathological features, 0.53 for the morpho-molecular and genetic features, 0.65 for the imaging features and 0.65 for the combined model including all parameters. Conclusions: Imaging-derived features represent an independent survival predictor in PDAC and enable the multiparametric, machine learning-assisted modelling of postoperative overall survival with a high performance compared to clinical and morpho-molecular/genetic parameters. We propose that future studies systematically include imaging-derived features to benchmark their additive value when evaluating biomarker-based model performance.

Cancer-related deaths are very commonly attributed to complications from metastases to neighboring as well as distant organs. Dissociate response in the treatment of pancreatic adenocarcinoma is one of the main causes of low treatment success and low survival rates. This behavior could not be explained by transcriptomics or genomics; however, differences in the composition at the protein level could be observed. We have characterized the proteomic composition of primary pancreatic adenocarcinoma and distant metastasis directly in human tissue samples, utilizing mass spectrometry imaging. The mass spectrometry data was used to train and validate machine learning models that could distinguish both tissue entities with an accuracy above 90%. Model validation on samples from another collection yielded a correct classification of both entities. Tentative identification of the discriminative molecular features showed that collagen fragments (COL1A1, COL1A2, and COL3A1) play a fundamental role in tumor development. From the analysis of the receiver operating characteristic, we could further advance some potential targets, such as histone and histone variations, that could provide a better understanding of tumor development, and consequently, more effective treatments.

The molecular carcinogenesis of intraductal tubulopapillary neoplasms (ITPN), recently described as rare neoplasms in the pancreato-biliary tract with a favorable prognosis despite a high incidence of associated pancreato-biliary adenocarcinoma, is still poorly understood. To identify driver genes, chromosomal gains and losses, mutational signatures, key signaling pathways, and potential therapeutic targets, the molecular profile of 11 biliary and 6 pancreatic ITPNs, associated with invasive adenocarcinoma in 14/17 cases, are studied by whole exome sequencing (WES). The WES of 17 ITPNs reveals common copy number variants (CNVs) broadly distributed across the genome, with recurrent chromosomal deletions primarily in 1p36 and 9p21 affecting the tumor suppressors CHD5 and CDKN2A, respectively, and gains in 1q affecting the prominent oncogene AKT3. The identified somatic nucleotide variants (SNVs) involve few core signaling pathways despite high genetic heterogeneity with diverse mutational spectra: Chromatin remodeling, the cell cycle, and DNA damage/repair. An OncoKB search identifies putative actionable genomic targets in 35% of the cases (6/17), including recurrent missense mutations of the FGFR2 gene in biliary ITPNs (2/11, 18%). Our results show that somatic SNV in classical cancer genes, typically associated with pancreato-biliary carcinogenesis, were absent (KRAS, IDH1/2, GNAS, and others) to rare (TP53 and SMAD4, 6%, respectively) in ITPNs. Mutational signature pattern analysis reveals a predominance of an age-related pattern. Our findings highlight that biliary ITPN and classical cholangiocarcinoma display commonalities, in particular mutations in genes of the chromatin remodeling pathway, and appear, therefore, more closely related than pancreatic ITPN and classical pancreatic ductal adenocarcinoma.

Knowing the precise location of analytes in the tissue has the potential to provide information about the organs’ function and predict its behavior. It is especially powerful when used in diagnosis and prognosis prediction of pathologies, such as cancer. Spatial proteomics, in particular mass spectrometry imaging, together with machine learning approaches, has been proven to be a very helpful tool in answering some histopathology conundrums. To gain accurate information about the tissue, there is a need to build robust classification models. We have investigated the impact of histological annotation on the classification accuracy of different tumor tissues. Intrinsic tissue heterogeneity directly impacts the efficacy of the annotations, having a more pronounced effect on more heterogeneous tissues, as pancreatic ductal adenocarcinoma, where the impact is over 20% in accuracy. On the other hand, in more homogeneous samples, such as kidney tumors, histological annotations have a slenderer impact on the classification accuracy.