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.
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.
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.
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.
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.
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.
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•Biliary tract cancers are clinically and genetically heterogeneous.•32 significantly mutated genes were identified, some negatively affecting prognosis.•A novel deletion of MUC17 at ...7q22.1 was detected.•Cell-of-origin predictions suggest hepatocyte-origin of hepatitis-related ICCs.•Deleterious germline mutations of cancer-predisposing genes were detected in 11% of patients with BTC.
Biliary tract cancers (BTCs) are clinically and pathologically heterogeneous and respond poorly to treatment. Genomic profiling can offer a clearer understanding of their carcinogenesis, classification and treatment strategy. We performed large-scale genome sequencing analyses on BTCs to investigate their somatic and germline driver events and characterize their genomic landscape.
We analyzed 412 BTC samples from Japanese and Italian populations, 107 by whole-exome sequencing (WES), 39 by whole-genome sequencing (WGS), and a further 266 samples by targeted sequencing. The subtypes were 136 intrahepatic cholangiocarcinomas (ICCs), 101 distal cholangiocarcinomas (DCCs), 109 peri-hilar type cholangiocarcinomas (PHCs), and 66 gallbladder or cystic duct cancers (GBCs/CDCs). We identified somatic alterations and searched for driver genes in BTCs, finding pathogenic germline variants of cancer-predisposing genes. We predicted cell-of-origin for BTCs by combining somatic mutation patterns and epigenetic features.
We identified 32 significantly and commonly mutated genes including TP53, KRAS, SMAD4, NF1, ARID1A, PBRM1, and ATR, some of which negatively affected patient prognosis. A novel deletion of MUC17 at 7q22.1 affected patient prognosis. Cell-of-origin predictions using WGS and epigenetic features suggest hepatocyte-origin of hepatitis-related ICCs. Deleterious germline mutations of cancer-predisposing genes such as BRCA1, BRCA2, RAD51D, MLH1, or MSH2 were detected in 11% (16/146) of BTC patients.
BTCs have distinct genetic features including somatic events and germline predisposition. These findings could be useful to establish treatment and diagnostic strategies for BTCs based on genetic information.
We here analyzed genomic features of 412 BTC samples from Japanese and Italian populations. A total of 32 significantly and commonly mutated genes were identified, some of which negatively affected patient prognosis, including a novel deletion of MUC17 at 7q22.1. Cell-of-origin predictions using WGS and epigenetic features suggest hepatocyte-origin of hepatitis-related ICCs. Deleterious germline mutations of cancer-predisposing genes were detected in 11% of patients with BTC. BTCs have distinct genetic features including somatic events and germline predisposition.
To analyze the prevalence of homologous recombination deficiency (HRD) in patients with pancreatic ductal adenocarcinoma (PDAC).
We conducted a systematic review and meta-analysis of the prevalence ...of HRD in PDAC from PubMed, Scopus, and Cochrane Library databases, and online cancer genomic data sets. The main outcome was pooled prevalence of somatic and germline mutations in the better characterized HRD genes (
,
,
,
,
,
,
, and the
genes). The secondary outcomes were prevalence of germline mutations overall, and in sporadic and familial cases; prevalence of germline
mutations in Ashkenazi Jewish (AJ); and prevalence of HRD based on other definitions (ie, alterations in other genes, genomic scars, and mutational signatures). Random-effects modeling with the Freeman-Tukey transformation was used for the analyses. PROSPERO registration number: (CRD42020190813).
Sixty studies with 21,842 participants were included in the systematic review and 57 in the meta-analysis. Prevalence of germline and somatic mutations was
: 0.9%,
: 3.5%,
: 0.2%,
: 2.2%,
: 0.3%,
: 0.5%,
: 0.0%, and
: 0.1%. Prevalence of germline mutations was
: 0.9% (2.4% in AJ),
: 3.8% (8.2% in AJ),
: 0.2%,
: 2%,
: 0.3%, and
: 0.4%. No significant differences between sporadic and familial cases were identified. HRD prevalence ranged between 14.5%-16.5% through targeted next-generation sequencing and 24%-44% through whole-genome or whole-exome sequencing allowing complementary genomic analysis, including genomic scars and other signatures (surrogate markers of HRD).
Surrogate readouts of HRD identify a greater proportion of patients with HRD than analyses limited to gene-level approaches. There is a clear need to harmonize HRD definitions and to validate the optimal biomarker for treatment selection. Universal HRD screening including integrated somatic and germline analysis should be offered to all patients with PDAC.
Histopathological samples are a treasure-trove of DNA for clinical research. However, the quality of DNA can vary depending on the source or extraction method applied. Thus a standardized and ...cost-effective workflow for the qualification of DNA preparations is essential to guarantee interlaboratory reproducible results. The qualification process consists of the quantification of double strand DNA (dsDNA) and the assessment of its suitability for downstream applications, such as high-throughput next-generation sequencing. We tested the two most frequently used instrumentations to define their role in this process: NanoDrop, based on UV spectroscopy, and Qubit 2.0, which uses fluorochromes specifically binding dsDNA. Quantitative PCR (qPCR) was used as the reference technique as it simultaneously assesses DNA concentration and suitability for PCR amplification. We used 17 genomic DNAs from 6 fresh-frozen (FF) tissues, 6 formalin-fixed paraffin-embedded (FFPE) tissues, 3 cell lines, and 2 commercial preparations. Intra- and inter-operator variability was negligible, and intra-methodology variability was minimal, while consistent inter-methodology divergences were observed. In fact, NanoDrop measured DNA concentrations higher than Qubit and its consistency with dsDNA quantification by qPCR was limited to high molecular weight DNA from FF samples and cell lines, where total DNA and dsDNA quantity virtually coincide. In partially degraded DNA from FFPE samples, only Qubit proved highly reproducible and consistent with qPCR measurements. Multiplex PCR amplifying 191 regions of 46 cancer-related genes was designated the downstream application, using 40 ng dsDNA from FFPE samples calculated by Qubit. All but one sample produced amplicon libraries suitable for next-generation sequencing. NanoDrop UV-spectrum verified contamination of the unsuccessful sample. In conclusion, as qPCR has high costs and is labor intensive, an alternative effective standard workflow for qualification of DNA preparations should include the sequential combination of NanoDrop and Qubit to assess the purity and quantity of dsDNA, respectively.
Background Limited data are available for pancreatic neuroendocrine carcinomas (NEC) defined by 2010 World Health Organization (WHO) criteria (mitotic count >20 mitoses/10 high-power fields and/or a ...Ki67 index of >20%), because most studies encompass heterogeneous cohorts of extrapulmonary/gastrointestinal NEC. Our aim was to evaluate the clinicopathologic characteristics, treatment, and prognosis of patients with pancreatic NEC defined by the 2010 WHO criteria. Methods We conducted a retrospective analysis of 59 patients with a histologic diagnosis of NEC between 1990 and 2012. All cases were re-reviewed and classified according to the WHO 2010 classification and the WHO 2000 criteria. Results All patients had stage III pancreatic NEC ( n = 34; 58%) or IV pancreatic NEC ( n = 25; 43%). Overall, 49 (83%) had poorly differentiated (PD) and 10 (17%) had a well-differentiated (WD) morphology. Fifteen patients (26%) were operated with curative intent (R0/R1), and 8 (14%) were R2 resections. Median disease-specific survival (DSS) for the entire cohort was 14 months. Median DSS did not differ between patient not undergoing resection and those undergoing R2 resection (10 vs 12 months; P > .46), but DSS was greater for patients who underwent R0/R1 resection compared with those with no resection/R2 resection (35 vs 11 months; P < .005). WD morphologic NEC had a greater survival than PD ones (43 vs 12 months; P = .004). Performance status, R2 resection/no resection, PD morphologic NEC, and no medical treatment were independent predictors of poor survival. Conclusion Pancreatic NEC constitute a heterogeneous group of tumors. Although NEC is an aggressive disease, curative resection in localized disease is associated with improved survival. Morphologic WD pancreatic NEC represents a subgroup with what seems to be a markedly improved survival. Within the NEC category, tumor treatment should be individualized considering tumor morphology as well as the other 2010 WHO criteria.
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