Explore the vast range of titles available.

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

DNA methylation of cytosines in CpG sites throughout the genome is an epigenetic mark contributing to gene expression regulation. DNA methylation patterns are specific to tissue type, conserved throughout life and reflect changes during tumorigenesis. DNA methylation recently emerged as a diagnostic tool to classify tumors based on a combination of preserved developmental and mutation induced signatures. In addition to the tumor classification, DNA methylation data can also be used to evaluate copy number variation, assess promoter methylation status of specific genes, such as MGMT or MLH1, and deconvolute the tumor microenvironment, assessing the tumor immune infiltrate as a potential biomarker for immunotherapy. Here we review the role for DNA methylation in tumor diagnosis.

Medulloblastoma is the most common malignant paediatric brain tumour, with ~30% mediated by Sonic hedgehog signalling. Vismodegib-mediated inhibition of the Sonic hedgehog effector Smoothened inhibits tumour growth but causes growth plate fusion at effective doses. Here, we report a nanotherapeutic approach targeting endothelial tumour vasculature to enhance blood–brain barrier crossing. We use fucoidan-based nanocarriers targeting endothelial P-selectin to induce caveolin-1-dependent transcytosis and thus nanocarrier transport into the brain tumour microenvironment in a selective and active manner, the efficiency of which is increased by radiation treatment. In a Sonic hedgehog medulloblastoma animal model, fucoidan-based nanoparticles encapsulating vismodegib exhibit a striking efficacy and marked reduced bone toxicity and drug exposure to healthy brain tissue. Overall, these findings demonstrate a potent strategy for targeted intracranial pharmacodelivery that overcomes the restrictive blood–brain barrier to achieve enhanced tumour-selective penetration and has therapeutic implications for diseases within the central nervous system.Targeting of tumour vasculature endothelial P-selectin promotes caveolin-1-mediated transcytosis for enhanced blood–brain barrier crossing of therapeutic nanoparticles against medulloblastoma.

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid (<90 seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma ( n  = 153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.3 ± 1.6%. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma. DeepGlioma, a multimodal deep learning approach for intraoperative diagnostic screening of diffuse glioma, trained on stimulated Raman histology and large-scale public genomic data, can predict molecular alterations for diffuse glioma diagnosis with high accuracy.

Recent genomic studies have shed light on the biology and inter-tumoral heterogeneity underlying pineal parenchymal tumors, in particular pineoblastomas (PBs) and pineal parenchymal tumors of intermediate differentiation (PPTIDs). Previous reports, however, had modest sample sizes and lacked the power to integrate molecular and clinical findings. The different proposed molecular group structures also highlighted a need to reach consensus on a robust and relevant classification system. We performed a meta-analysis on 221 patients with molecularly characterized PBs and PPTIDs. DNA methylation profiles were analyzed through complementary bioinformatic approaches and molecular subgrouping was harmonized. Demographic, clinical, and genomic features of patients and samples from these pineal tumor groups were annotated. Four clinically and biologically relevant consensus PB groups were defined: PB-miRNA1 (n = 96), PB-miRNA2 (n = 23), PB-MYC/FOXR2 (n = 34), and PB-RB1 (n = 25). A final molecularly distinct group, designated PPTID (n = 43), comprised histological PPTID and PBs. Genomic and transcriptomic profiling allowed the characterization of oncogenic drivers for individual tumor groups, specifically, alterations in the microRNA processing pathway in PB-miRNA1/2, MYC amplification and FOXR2 overexpression in PB-MYC/FOXR2, RB1 alteration in PB-RB1, and KBTBD4 insertion in PPTID. Age at diagnosis, sex predilection, and metastatic status varied significantly among tumor groups. While patients with PB-miRNA2 and PPTID had superior outcome, survival was intermediate for patients with PB-miRNA1, and dismal for those with PB-MYC/FOXR2 or PB-RB1. Reduced-dose CSI was adequate for patients with average-risk, PB-miRNA1/2 disease. We systematically interrogated the clinical and molecular heterogeneity within pineal parenchymal tumors and proposed a consensus nomenclature for disease groups, laying the groundwork for future studies as well as routine use in tumor diagnostic classification and clinical trial stratification.

Sinonasal undifferentiated carcinoma (SNUC) is an aggressive malignancy harboring IDH2 R172 mutations in >80% cases. We explored the potential of genome-wide DNA methylation profiling to elucidate tumor biology and improve the diagnosis of sinonasal undifferentiated carcinoma and its histologic mimics. Forty-two cases, including sinonasal undifferentiated, large cell neuroendocrine, small cell neuroendocrine, and SMARCB1-deficient carcinomas and olfactory neuroblastoma, were profiled by Illumina Infinium Methylation EPIC array interrogating >850,000 CpG sites. The data were analyzed using a custom bioinformatics pipeline. IDH2 mutation status was determined by the targeted exome sequencing (MSK-IMPACT TM ) in most cases. H3K27 methylation level was assessed by the immunohistochemistry-based H -score. DNA methylation-based semi-supervised hierarchical clustering analysis segregated IDH2 mutants, mostly sinonasal undifferentiated ( n  = 10) and large cell neuroendocrine carcinomas ( n  = 4), from other sinonasal tumors, and formed a single cluster irrespective of the histologic type. t-distributed stochastic neighbor embedding dimensionality reduction analysis showed no overlap between IDH2 mutants, SMARCB1-deficient carcinoma and olfactory neuroblastoma. IDH2 mutants demonstrated a global methylation phenotype and an increase in repressive trimethylation of H3K27 in comparison to IDH2 wild-type tumors ( p  < 0.001). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed no difference in pathway activation between IDH2 -mutated sinonasal undifferentiated and large cell neuroendocrine carcinomas. In comparison to SMARCB1-deficient, IDH2 -mutated carcinomas were associated with better disease-free survival ( p  = 0.034) and lower propensity for lung metastasis ( p  = 0.002). ARID1A mutations were common in small cell neuroendocrine carcinoma but not among IDH2 mutants (3/3 versus 0/18 and p  < 0.001). IDH2 mutations in sinonasal carcinomas induce a hypermethylator phenotype and define a molecular subgroup of tumors arising in this location. IDH2 -mutated sinonasal undifferentiated carcinoma and large cell neuroendocrine carcinoma likely represent a phenotypic spectrum of the same entity, which is distinct from small cell neuroendocrine and SMARCB1-deficient sinonasal carcinomas. DNA methylation-based analysis of the sinonasal tumors has potential to improve the diagnostic accuracy and classification of tumors arising in this location.

Inhibition of the vascular endothelial growth factor (VEGF) pathway has failed to improve overall survival of patients with glioblastoma (GBM). We previously showed that angiopoietin-2 (Ang-2) overexpression compromised the benefit from anti-VEGF therapy in a preclinical GBM model. Here we investigated whether dual Ang-2/VEGF inhibition could overcome resistance to anti-VEGF treatment. We treated mice bearing orthotopic syngeneic (Gl261) GBMs or human (MGG8) GBMxenografts with antibodies inhibiting VEGF (B20), or Ang-2/VEGF (CrossMab, A2V). We examined the effects of treatment on the tumor vasculature, immune cell populations, tumor growth, and survival in both the Gl261 and MGG8 tumor models. We found that in the Gl261 model, which displays a highly abnormal tumor vasculature, A2V decreased vessel density, delayed tumor growth, and prolonged survival compared with B20. In the MGG8 model, which displays a low degree of vessel abnormality, A2V induced no significant changes in the tumor vasculature but still prolonged survival. In both the Gl261 and MGG8 models A2V reprogrammed protumor M2 macrophages toward the antitumor M1 phenotype. Our findings indicate that A2V may prolong survival in mice with GBM by reprogramming the tumor immune microenvironment and delaying tumor growth.

Metastatic cancer cells (seeds) preferentially grow in the secondary sites with a permissive microenvironment (soil). We show that the metastatic cells can bring their own soil—stromal components including activated fibroblasts—from the primary site to the lungs. By analyzing the efferent blood from tumors, we found that viability of circulating metastatic cancer cells is higher if they are incorporated in heterotypic tumor-stroma cell fragments. Moreover, we show that these cotraveling stromal cells provide an early growth advantage to the accompanying metastatic cancer cells in the lungs. Consistent with this hypothesis, we demonstrate that partial depletion of the carcinoma-associated fibroblasts, which spontaneously spread to the lung tissue along with metastatic cancer cells, significantly decreases the number of metastases and extends survival after primary tumor resection. Finally, we show that the brain metastases from lung carcinoma and other carcinomas in patients contain carcinoma-associated fibroblasts, in contrast to primary brain tumors or normal brain tissue. Demonstration of the direct involvement of primary tumor stroma in metastasis has important conceptual and clinical implications for the colonization step in tumor progression.

Glioblastomas (GBMs) rapidly become refractory to anti-VEGF therapies. We previously demonstrated that ectopic overexpression of angiopoietin-2 (Ang-2) compromises the benefits of anti-VEGF receptor (VEGFR) treatment in murine GBM models and that circulating Ang-2 levels in GBM patients rebound after an initial decrease following cediranib (a pan-VEGFR tyrosine kinase inhibitor) administration. Here we tested whether dual inhibition of VEGFR/Ang-2 could improve survival in two orthotopic models of GBM, Gl261 and U87. Dual therapy using cediranib and MEDI3617 (an anti–Ang-2–neutralizing antibody) improved survival over each therapy alone by delaying Gl261 growth and increasing U87 necrosis, effectively reducing viable tumor burden. Consistent with their vascular-modulating function, the dual therapies enhanced morphological normalization of vessels. Dual therapy also led to changes in tumor-associated macrophages (TAMs). Inhibition of TAM recruitment using an anti–colony-stimulating factor-1 antibody compromised the survival benefit of dual therapy. Thus, dual inhibition of VEGFR/Ang-2 prolongs survival in preclinical GBM models by reducing tumor burden, improving normalization, and altering TAMs. This approach may represent a potential therapeutic strategy to overcome the limitations of anti-VEGFR monotherapy in GBM patients by integrating the complementary effects of anti-Ang2 treatment on vessels and immune cells.

Programmed cell death protein-1 (PD-1) checkpoint immunotherapy efficacy remains unpredictable in glioblastoma (GBM) patients due to the genetic heterogeneity and immunosuppressive tumor microenvironments. Here, we report a microfluidics-based, patient-specific ‘GBM-on-a-Chip’ microphysiological system to dissect the heterogeneity of immunosuppressive tumor microenvironments and optimize anti-PD-1 immunotherapy for different GBM subtypes. Our clinical and experimental analyses demonstrated that molecularly distinct GBM subtypes have distinct epigenetic and immune signatures that may lead to different immunosuppressive mechanisms. The real-time analysis in GBM-on-a-Chip showed that mesenchymal GBM niche attracted low number of allogeneic CD154+CD8+ T-cells but abundant CD163+ tumor-associated macrophages (TAMs), and expressed elevated PD-1/PD-L1 immune checkpoints and TGF-β1, IL-10, and CSF-1 cytokines compared to proneural GBM. To enhance PD-1 inhibitor nivolumab efficacy, we co-administered a CSF-1R inhibitor BLZ945 to ablate CD163+ M2-TAMs and strengthened CD154+CD8+ T-cell functionality and GBM apoptosis on-chip. Our ex vivo patient-specific GBM-on-a-Chip provides an avenue for a personalized screening of immunotherapies for GBM patients.