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Mickey Mouse : the 90th anniversary collection
\"Oh, fer gosh sakes!\" Mickey's celebrating and he's joined by all the gang! Goofy, Minnie, Peg-Leg Pete, and Atomo Bleep-Bleep are all here to celebrate his big day in style! Includes the thrilling \"Sacred Spring of Seasons Past,\" \"Boxing Champion,\" \"Return of the Phantom Blot,\" and more! Brought to you by fan-favorite creators such as Floyd Gottfredson, Romano Scarpa, Paul Murry, Byron Erickson, Andrea \"Casty\" Castellan, and more.
Book
Artificial intelligence in oncology: current applications and future perspectives
Artificial intelligence (AI) is concretely reshaping the landscape and horizons of oncology, opening new important opportunities for improving the management of cancer patients. Analysing the AI-based devices that have already obtained the official approval by the Federal Drug Administration (FDA), here we show that cancer diagnostics is the oncology-related area in which AI is already entered with the largest impact into clinical practice. Furthermore, breast, lung and prostate cancers represent the specific cancer types that now are experiencing more advantages from AI-based devices. The future perspectives of AI in oncology are discussed: the creation of multidisciplinary platforms, the comprehension of the importance of all neoplasms, including rare tumours and the continuous support for guaranteeing its growth represent in this time the most important challenges for finalising the ‘AI-revolution’ in oncology.
Journal Article
Genetics and Epigenetics of Gastroenteropancreatic Neuroendocrine Neoplasms
Abstract
Gastroenteropancreatic (GEP) neuroendocrine neoplasms (NENs) are heterogeneous regarding site of origin, biological behavior, and malignant potential. There has been a rapid increase in data publication during the last 10 years, mainly driven by high-throughput studies on pancreatic and small intestinal neuroendocrine tumors (NETs). This review summarizes the present knowledge on genetic and epigenetic alterations. We integrated the available information from each compartment to give a pathway-based overview. This provided a summary of the critical alterations sustaining neoplastic cells. It also highlighted similarities and differences across anatomical locations and points that need further investigation. GEP-NENs include well-differentiated NETs and poorly differentiated neuroendocrine carcinomas (NECs). NENs are graded as G1, G2, or G3 based on mitotic count and/or Ki-67 labeling index, NECs are G3 by definition. The distinction between NETs and NECs is also linked to their genetic background, as TP53 and RB1 inactivation in NECs set them apart from NETs. A large number of genetic and epigenetic alterations have been reported. Recurrent changes have been traced back to a reduced number of core pathways, including DNA damage repair, cell cycle regulation, and phosphatidylinositol 3-kinase/mammalian target of rapamycin signaling. In pancreatic tumors, chromatin remodeling/histone methylation and telomere alteration are also affected. However, also owing to the paucity of disease models, further research is necessary to fully integrate and functionalize data on deregulated pathways to recapitulate the large heterogeneity of behaviors displayed by these tumors. This is expected to impact diagnostics, prognostic stratification, and planning of personalized therapy.
Journal Article
Bioengineered 3D models of human pancreatic cancer recapitulate in vivo tumour biology
Patient-derived in vivo models of human cancer have become a reality, yet their turnaround time is inadequate for clinical applications. Therefore, tailored ex vivo models that faithfully recapitulate in vivo tumour biology are urgently needed. These may especially benefit the management of pancreatic ductal adenocarcinoma (PDAC), where therapy failure has been ascribed to its high cancer stem cell (CSC) content and high density of stromal cells and extracellular matrix (ECM). To date, these features are only partially reproduced ex vivo using organoid and sphere cultures. We have now developed a more comprehensive and highly tuneable ex vivo model of PDAC based on the 3D co-assembly of peptide amphiphiles (PAs) with custom ECM components (PA-ECM). These cultures maintain patient-specific transcriptional profiles and exhibit CSC functionality, including strong in vivo tumourigenicity. User-defined modification of the system enables control over niche-dependent phenotypes such as epithelial-to-mesenchymal transition and matrix deposition. Indeed, proteomic analysis of these cultures reveals improved matrisome recapitulation compared to organoids. Most importantly, patient-specific in vivo drug responses are better reproduced in self-assembled cultures than in other models. These findings support the use of tuneable self-assembling platforms in cancer research and pave the way for future precision medicine approaches.
Personalized cancer medicine currently lacks custom platforms that mimic the microenvironment of human tissues. Here, the authors show how self-assembled patient-derived models of pancreatic cancer recapitulate key biological features of the original tumours such as matrix composition and stemness.
Journal Article
Co-occurring IPMN and pancreatic cancer: the same or different? An overview from histology to molecular pathology
Intraductal papillary mucinous neoplasm (IPMN) of the pancreas is one of the most well-established precursors of pancreatic cancer. Its progression to acquire invasiveness is a complex process, based on the accumulation of morphological and genetic alterations. Recent advances in DNA sequencing also showed that co-occurring IPMNs and pancreatic cancers could be totally independent, further complicating our understanding of this complex scenario. The distinction between IPMN and related pancreatic cancer vs IPMN and co-occurring—but not related—pancreatic cancer is a challenging task in routine diagnostic activity, but may have important implications for precision oncology. Of note, recent multiregional sequencing-based studies focused not only on IPMN multi-step tumourigenesis, but also on the divergent intratumoural heterogeneity of this neoplasm. Globally considered, there are three different situations in which co-occurring IPMNs and invasive carcinomas can be found in the same pancreata, indicated with different terminologies: (1) IPMN-associated carcinoma: this definition indicates a carcinoma arising from an IPMN and can be also defined as IPMN-derived carcinoma, sequential or likely related; (2) independent IPMN and invasive carcinoma: the two lesions are not related, and this situation is defined as concomitant, de novo or likely independent; (3) branch-off pathway, where an invasive carcinoma and an adjacent IPMN develop divergently in a forked fashion from a common ancestral clone. In this review, we aim at clarifying the most important nomenclature/definitions of these different situations, also providing an overview of the molecular state-of-the-art and of the clinical implications of this complex landscape.
Journal Article
To metabolomics and beyond: a technological portfolio to investigate cancer metabolism
Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.
Journal Article
Differential Activity of Nivolumab, Pembrolizumab and MPDL3280A according to the Tumor Expression of Programmed Death-Ligand-1 (PD-L1): Sensitivity Analysis of Trials in Melanoma, Lung and Genitourinary Cancers
The potential predictive role of programmed death-ligand-1 (PD-L1) expression on tumor cells in the context of solid tumor treated with checkpoint inhibitors targeting the PD-1 pathway represents an issue for clinical research.
Overall response rate (ORR) was extracted from phase I-III trials investigating nivolumab, pembrolizumab and MPDL3280A for advanced melanoma, non-small cell lung cancer (NSCLC) and genitourinary cancer, and cumulated by adopting a fixed and random-effect model with 95% confidence interval (CI). Interaction test according to tumor PD-L1 was accomplished. A sensitivity analysis according to adopted drug, tumor type, PD-L1 cut-off and treatment line was performed.
Twenty trials (1,475 patients) were identified. A significant interaction (p<0.0001) according to tumor PD-L1 expression was found in the overall sample with an ORR of 34.1% (95% CI 27.6-41.3%) in the PD-L1 positive and 19.9% (95% CI 15.4-25.3%) in the PD-L1 negative population. ORR was significantly higher in PD-L1 positive in comparison to PD-L1 negative patients for nivolumab and pembrolizumab, with an absolute difference of 16.4% and 19.5%, respectively. A significant difference in activity of 22.8% and 8.7% according to PD-L1 was found for melanoma and NSCLC, respectively, with no significant difference for genitourinary cancer.
Overall, the three antibodies provide a significant differential effect in terms of activity according to PD-L1 expression on tumor cells. The predictive value of PD-L1 on tumor cells seems to be more robust for anti-PD-1 antibody (nivolumab and pembrolizumab), and in the context of advanced melanoma and NSCLC.
Journal Article
CT Enhancement and 3D Texture Analysis of Pancreatic Neuroendocrine Neoplasms
To evaluate pancreatic neuroendocrine neoplasms (panNENs) grade prediction by means of qualitative and quantitative CT evaluation, and 3D CT-texture analysis. Patients with histopathologically-proven panNEN, availability of Ki67% values and pre-treatment CT were included. CT images were retrospectively reviewed, and qualitative and quantitative images analysis were done; for quantitative analysis four enhancement-ratios and three permeability-ratios were created. 3D CT-texture imaging analysis was done (Mean Value; Variance; Skewness; Kurtosis; Entropy). Subsequently, these features were compared among the three grading (G) groups. 304 patients affected by panNENs were considered, and 100 patients were included. At qualitative evaluation, frequency of irregular margins was significantly different between tumor G groups. At quantitative evaluation, for all ratios, comparisons resulted statistical significant different between G1 and G3 groups and between G2 and G3 groups. At 3D CT-texture analysis, Kurtosis resulted statistical significant different among three G groups and Entropy resulted statistical significant different between G1 and G3 and between G2 and G3 groups. Quantitative CT evaluation of panNENs can predict tumor grade, discerning G1 from G3 and G2 from G3 tumors. CT-texture analysis can predict panNENs tumor grade, distinguishing G1 from G3 and G2 from G3, and G1 from G2 tumors.
Journal Article
A common classification framework for neuroendocrine neoplasms: an International Agency for Research on Cancer (IARC) and World Health Organization (WHO) expert consensus proposal
The classification of neuroendocrine neoplasms (NENs) differs between organ systems and currently causes considerable confusion. A uniform classification framework for NENs at any anatomical location may reduce inconsistencies and contradictions among the various systems currently in use. The classification suggested here is intended to allow pathologists and clinicians to manage their patients with NENs consistently, while acknowledging organ-specific differences in classification criteria, tumor biology, and prognostic factors. The classification suggested is based on a consensus conference held at the International Agency for Research on Cancer (IARC) in November 2017 and subsequent discussion with additional experts. The key feature of the new classification is a distinction between differentiated neuroendocrine tumors (NETs), also designated carcinoid tumors in some systems, and poorly differentiated NECs, as they both share common expression of neuroendocrine markers. This dichotomous morphological subdivision into NETs and NECs is supported by genetic evidence at specific anatomic sites as well as clinical, epidemiologic, histologic, and prognostic differences. In many organ systems, NETs are graded as G1, G2, or G3 based on mitotic count and/or Ki-67 labeling index, and/or the presence of necrosis; NECs are considered high grade by definition. We believe this conceptual approach can form the basis for the next generation of NEN classifications and will allow more consistent taxonomy to understand how neoplasms from different organ systems inter-relate clinically and genetically.
Journal Article
Artificial intelligence–based algorithms for the diagnosis of prostate cancer: A systematic review
Abstract
Objectives
The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging (WSI) and artificial intelligence (AI) have both gained approval for primary diagnosis in prostate pathology, providing physicians with novel tools for their daily routine.
Methods
A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was carried out in electronic databases to gather the available evidence on the application of AI-based algorithms to prostate cancer.
Results
Of 6290 articles, 80 were included, mostly (59%) dealing with biopsy specimens. Glass slides were digitized to WSI in most studies (89%), roughly two-thirds of which (66%) exploited convolutional neural networks for computational analysis. The algorithms achieved good to excellent results about cancer detection and grading, along with significantly reduced TATs. Furthermore, several studies showed a relevant correlation between AI-identified histologic features and prognostic predictive variables such as biochemical recurrence, extraprostatic extension, perineural invasion, and disease-free survival.
Conclusions
The published evidence suggests that AI can be reliably used for prostate cancer detection and grading, assisting pathologists in the time-consuming screening of slides. Further technologic improvement would help widening AI’s adoption in prostate pathology, as well as expanding its prognostic predictive potential.
Journal Article