Lee Cooper

BACKGROUND: Diffuse low-grade and intermediate-grade gliomas (which together make up the lower-grade gliomas, World Health Organization grades II and III) have highly variable clinical behavior that is not adequately predicted on the basis of histologic class. Some are indolent; others quickly progress to glioblastoma. The uncertainty is compounded by interobserver variability in histologic diagnosis. Mutations in IDH, TP53, and ATRX and codeletion of chromosome arms 1p and 19q (1p/19q codeletion) have been implicated as clinically relevant markers of lower-grade gliomas. METHODS: We performed genomewide analyses of 293 lower-grade gliomas from adults, incorporating exome sequence, DNA copy number, DNA methylation, messenger RNA expression, microRNA expression, and targeted protein expression. These data were integrated and tested for correlation with clinical outcomes. RESULTS: Unsupervised clustering of mutations and data from RNA, DNA-copy-number, and DNA-methylation platforms uncovered concordant classification of three robust, nonoverlapping, prognostically significant subtypes of lower-grade glioma that were captured more accurately by IDH, 1p/19q, and TP53 status than by histologic class. Patients who had lower-grade gliomas with an IDH mutation and 1p/19q codeletion had the most favorable clinical outcomes. Their gliomas harbored mutations in CIC, FUBP1, NOTCH1, and the TERT promoter. Nearly all lower-grade gliomas with IDH mutations and no 1p/19q codeletion had mutations in TP53 (94%) and ATRX inactivation (86%). The large majority of lower-grade gliomas without an IDH mutation had genomic aberrations and clinical behavior strikingly similar to those found in primary glioblastoma. CONCLUSIONS: The integration of genomewide data from multiple platforms delineated three molecular classes of lower-grade gliomas that were more concordant with IDH, 1p/19q, and TP53 status than with histologic class. Lower-grade gliomas with an IDH mutation either had 1p/19q codeletion or carried a TP53 mutation. Most lower-grade gliomas without an IDH mutation were molecularly and clinically similar to glioblastoma. (Funded by the National Institutes of Health.).

Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma. We use statistical sampling techniques to address challenges in learning survival from histology images, including tumor heterogeneity and the need for large training cohorts. We also provide insights into the prediction mechanisms of SCNNs, using heat map visualization to show that SCNNs recognize important structures, like microvascular proliferation, that are related to prognosis and that are used by pathologists in grading. These results highlight the emerging role of deep learning in precision medicine and suggest an expanding utility for computational analysis of histology in the future practice of pathology.

Inhibition of vascular endothelial growth factor-A (VEGF) signaling is a promising therapeutic approach that aims to stabilize the progression of solid malignancies by abrogating tumor-induced angiogenesis. This may be accomplished by inhibiting the kinase activity of VEGF receptor-2 (KDR), which has a key role in mediating VEGF-induced responses. The novel indole-ether quinazoline AZD2171 is a highly potent (IC50 < 1 nmol/L) ATP-competitive inhibitor of recombinant KDR tyrosine kinase in vitro. Concordant with this activity, in human umbilical vein endothelial cells, AZD2171 inhibited VEGF-stimulated proliferation and KDR phosphorylation with IC50 values of 0.4 and 0.5 nmol/L, respectively. In a fibroblast/endothelial cell coculture model of vessel sprouting, AZD2171 also reduced vessel area, length, and branching at subnanomolar concentrations. Once-daily oral administration of AZD2171 ablated experimental (VEGF-induced) angiogenesis in vivo and inhibited endochondral ossification in bone or corpora luteal development in ovary; physiologic processes that are highly dependent upon neovascularization. The growth of established human tumor xenografts (colon, lung, prostate, breast, and ovary) in athymic mice was inhibited dose-dependently by AZD2171, with chronic administration of 1.5 mg per kg per day producing statistically significant inhibition in all models. A histologic analysis of Calu-6 lung tumors treated with AZD2171 revealed a reduction in microvessel density within 52 hours that became progressively greater with the duration of treatment. These changes are indicative of vascular regression within tumors. Collectively, the data obtained with AZD2171 are consistent with potent inhibition of VEGF signaling, angiogenesis, neovascular survival, and tumor growth. AZD2171 is being developed clinically as a once-daily oral therapy for the treatment of cancer.

Nature Communications 8: Article number: 14356 (2017); Published: 16 February 2017; Updated: 18 April 2017 The original version of this Article contained an error in the spelling of the author Carlos S. Moreno, which was incorrectly given as Carlos Moreno. This has now been corrected in both the PDFand HTML versions of the Article.