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Development of precision medicine for malignant pleural mesothelioma (MPM) requires a deep knowledge of tumor heterogeneity. Histologic and molecular classifications and histo‐molecular gradients have been proposed to describe heterogeneity, but a deeper understanding of gene mutations in the context of MPM heterogeneity is required and the associations between mutations and clinical data need to be refined. We characterized genetic alterations on one of the largest MPM series (266 tumor samples), well annotated with histologic, molecular and clinical data of patients. Targeted next‐generation sequencing was performed focusing on the major MPM mutated genes and the TERT promoter. Molecular heterogeneity was characterized using predictors allowing classification of each tumor into the previously described molecular subtypes and the determination of the proportion of epithelioid‐like and sarcomatoid‐like components (E/S.scores). The mutation frequencies are consistent with literature data, but this study emphasized that TERT promoter, not considered by previous large sequencing studies, was the third locus most affected by mutations in MPM. Mutations in TERT promoter, NF2, and LATS2 were more frequent in nonepithelioid MPM and positively associated with the S.score. BAP1, NF2, TERT promoter, TP53, and SETD2 mutations were enriched in some molecular subtypes. NF2 mutation rate was higher in asbestos unexposed patient. TERT promoter, NF2, and TP53 mutations were associated with a poorer overall survival. Our findings lead to a better characterization of MPM heterogeneity by identifying new significant associations between mutational status and histologic and molecular heterogeneity. Strikingly, we highlight the strong association between new mutations and overall survival. Our comprehensive genetic characterization of key‐altered genes including TERT promoter in malignant pleural mesothelioma led to the identification of new significant associations between the mutational status and the histological and molecular classifications. Our findings allow a better understanding of the genetic landscape in the context of tumor heterogeneity and highlight the high prognostic value of gene mutations in mesothelioma.

Recently, guidelines for endometrial cancer (EC) were released that guide treatment decisions according to the tumors’ molecular profiles. To date, no real-world data regarding the clinical feasibility of molecular profiling have been released. This retrospective, monocentric study investigated the clinical feasibility of molecular profiling and its potential impact on treatment decisions. Tumor specimens underwent molecular profiling (testing for genetic alterations, (immune-)histological examination of lymphovascular space invasion (LVSI), and L1CAM) as part of the clinical routine and were classified according to the European Society for Medical Oncology (ESMO) classification system and to an integrated molecular risk stratification. Shifts between risk groups and potential treatment alterations are described. A total of 60 cases were included, of which twelve were excluded (20%), and eight of the remaining 48 were not characterized (drop-out rate of 16.7%). Molecular profiling revealed 4, 6, 25, and 5 patients with DNA polymerase-epsilon mutation, microsatellite instability, no specific molecular profile, and TP53 mutation, respectively. Three patients had substantial LVSI, and four patients showed high L1CAM expression. Molecular profiling took a median of 18.5 days. Substantial shifts occurred between the classification systems: four patients were upstaged, and 19 patients were downstaged. Molecular profiling of EC specimens is feasible in a daily routine, and new risk classification systems will change treatment decisions substantially.

Background This paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Currently, many available glioma datasets often contain some unlabeled brain scans, and many datasets are moderate in size. Methods We propose to exploit deep semi-supervised learning to make full use of the unlabeled data. Deep CNN features were incorporated into a new graph-based semi-supervised learning framework for learning the labels of the unlabeled data, where a new 3D-2D consistent constraint is added to make consistent classifications for the 2D slices from the same 3D brain scan. A deep-learning classifier is then trained to classify different glioma types using both labeled and unlabeled data with estimated labels. To alleviate the overfitting caused by moderate-size datasets, synthetic MRIs generated by Generative Adversarial Networks (GANs) are added in the training of CNNs. Results The proposed scheme has been tested on two glioma datasets, TCGA dataset for IDH-mutation prediction (molecular-based glioma subtype classification) and MICCAI dataset for glioma grading. Our results have shown good performance (with test accuracies 86.53% on TCGA dataset and 90.70% on MICCAI dataset). Conclusions The proposed scheme is effective for glioma IDH-mutation prediction and glioma grading, and its performance is comparable to the state-of-the-art.

This review deals with studies that have used cDNA microarrays and immunohistochemistry to identify a subtype of breast carcinoma known as basal-like carcinoma. The key breast carcinoma studies are critically discussed to highlight methodological problems in cohort selection, definitions, interpretation of results and statistical analysis. The review concludes that basal-like carcinomas do not reflect a single, biologically uniform group of breast cancers, but show significant variations in their phenotypes, grades, immunoprofiles and clinical behavior, just as a wide range of subtypes and behaviors is observed among epithelial/luminal-derived breast carcinomas. Well-designed studies with comparison of low-grade nonbasal versus low-grade basal and high-grade nonbasal versus high-grade basal carcinomas are necessary before one can be convinced that this subtype represents a distinct clinicopathological entity.

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them—STK11, EGFR, FAT1, SETBP1, KRAS and TP53—can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH . A convolutional neural network model using feature extraction and machine-learning techniques provides a tool for classification of lung cancer histopathology images and predicting mutational status of driver oncogenes

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.

Functional genomics approaches can overcome limitations—such as the lack of identification of robust targets and poor clinical efficacy—that hamper cancer drug development. Here we performed genome-scale CRISPR–Cas9 screens in 324 human cancer cell lines from 30 cancer types and developed a data-driven framework to prioritize candidates for cancer therapeutics. We integrated cell fitness effects with genomic biomarkers and target tractability for drug development to systematically prioritize new targets in defined tissues and genotypes. We verified one of our most promising dependencies, the Werner syndrome ATP-dependent helicase, as a synthetic lethal target in tumours from multiple cancer types with microsatellite instability. Our analysis provides a resource of cancer dependencies, generates a framework to prioritize cancer drug targets and suggests specific new targets. The principles described in this study can inform the initial stages of drug development by contributing to a new, diverse and more effective portfolio of cancer drug targets. In a screen of 324 human cancer cell lines and utilising a systematic target prioritization framework, the Werner syndrome ATP-dependent helicase is shown to be a synthetic lethal target in tumours from multiple cancer types with microsatellite instability, providing a new target for cancer drug development.

In the last two decades, the increasing availability of technologies for molecular analyses has allowed an insight in the genomic alterations of neuroendocrine neoplasms (NEN) of the gastrointestinal tract and pancreas. This knowledge has confirmed, supported, and informed the pathological classification of NEN, clarifying the differences between neuroendocrine carcinomas (NEC) and neuroendocrine tumors (NET) and helping to define the G3 NET category. At the same time, the identification genomic alterations, in terms of gene mutation, structural abnormalities, and epigenetic changes differentially involved in the pathogenesis of NEC and NET has identified potential molecular targets for precision therapy. This review critically recapitulates the available molecular features of digestive NEC and NET, highlighting their correlates with pathological aspects and clinical characteristics of these neoplasms and revising their role as predictive biomarkers for targeted therapy. In this context, the feasibility and applicability of a molecular classification of gastrointestinal and pancreatic NEN will be explored.

Sequencing of circulating tumor DNA from cancer patients is a cost-efficient approach with turnaround time compatible with clinical practice to inform treatment decision-making in a phase 1 trial settingNext-generation sequencing (NGS) of circulating tumor DNA (ctDNA) supports blood-based genomic profiling but is not yet routinely implemented in the setting of a phase I trials clinic. TARGET is a molecular profiling program with the primary aim to match patients with a broad range of advanced cancers to early phase clinical trials on the basis of analysis of both somatic mutations and copy number alterations (CNA) across a 641 cancer-associated-gene panel in a single ctDNA assay. For the first 100 TARGET patients, ctDNA data showed good concordance with matched tumor and results were turned round within a clinically acceptable timeframe for Molecular Tumor Board (MTB) review. When a 2.5% variant allele frequency (VAF) threshold was applied, actionable mutations were identified in 41 of 100 patients, and 11 of these patients received a matched therapy. These data support the application of ctDNA in this early phase trial setting where broad genomic profiling of contemporaneous tumor material enhances patient stratification to novel therapies and provides a practical template for bringing routinely applied blood-based analyses to the clinic.

The use of liquid biopsies for cancer detection and management is rapidly gaining prominence 1 . Current methods for the detection of circulating tumour DNA involve sequencing somatic mutations using cell-free DNA, but the sensitivity of these methods may be low among patients with early-stage cancer given the limited number of recurrent mutations 2 – 5 . By contrast, large-scale epigenetic alterations—which are tissue- and cancer-type specific—are not similarly constrained 6 and therefore potentially have greater ability to detect and classify cancers in patients with early-stage disease. Here we develop a sensitive, immunoprecipitation-based protocol to analyse the methylome of small quantities of circulating cell-free DNA, and demonstrate the ability to detect large-scale DNA methylation changes that are enriched for tumour-specific patterns. We also demonstrate robust performance in cancer detection and classification across an extensive collection of plasma samples from several tumour types. This work sets the stage to establish biomarkers for the minimally invasive detection, interception and classification of early-stage cancers based on plasma cell-free DNA methylation patterns. An immunoprecipitation-based protocol is developed to analyse DNA methylation in small quantities of circulating cell-free DNA, and can detect and classify cancers in plasma samples from several tumour types.