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- Christofer Juhlin, C., et al.
(författare)
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Whole-exome sequencing defines the mutational landscape of pheochromocytoma and identifies KMT2D as a recurrently mutated gene
- 2015
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Ingår i: Genes, Chromosomes and Cancer. - : Wiley: 12 months. - 1045-2257 .- 1098-2264. ; 54:9, s. 542-554
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Tidskriftsartikel (refereegranskat)abstract
- As subsets of pheochromocytomas (PCCs) lack a defined molecular etiology, we sought to characterize the mutational landscape of PCCs to identify novel gene candidates involved in disease development. A discovery cohort of 15 PCCs wild type for mutations in PCC susceptibility genes underwent whole-exome sequencing, and an additional 83 PCCs served as a verification cohort for targeted sequencing of candidate mutations. A low rate of nonsilent single nucleotide variants (SNVs) was detected (6.1/sample). Somatic HRAS and EPAS1 mutations were observed in one case each, whereas the remaining 13 cases did not exhibit variants in established PCC genes. SNVs aggregated in apoptosis-related pathways, and mutations in COSMIC genes not previously reported in PCCs included ZAN, MITF, WDTC1, and CAMTA1. Two somatic mutations and one constitutional variant in the well-established cancer gene lysine (K)-specific methyltransferase 2D (KMT2D, MLL2) were discovered in one sample each, prompting KMT2D screening using focused exome-sequencing in the verification cohort. An additional 11 PCCs displayed KMT2D variants, of which two were recurrent. In total, missense KMT2D variants were found in 14 (11 somatic, two constitutional, one undetermined) of 99 PCCs (14%). Five cases displayed somatic mutations in the functional FYR/SET domains of KMT2D, constituting 36% of all KMT2D-mutated PCCs. KMT2D expression was upregulated in PCCs compared to normal adrenals, and KMT2D overexpression positively affected cell migration in a PCC cell line. We conclude that KMT2D represents a recurrently mutated gene with potential implication for PCC development. (c) 2015 The Authors. Genes, Chromosomes and Cancer Published by Wiley Periodicals, Inc.
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- Bychkov, D, et al.
(författare)
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Deep learning based tissue analysis predicts outcome in colorectal cancer
- 2018
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 8:1, s. 3395-
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Tidskriftsartikel (refereegranskat)abstract
- Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79–3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28–2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30–2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.
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