Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes

Dentro, Stefan C.; Leshchiner, Ignaty; Haase, Kerstin; Tarabichi, Maxime; Wintersinger, Jeff; Deshwar, Amit G.; Yu, Kaixian; Rubanova, Yulia; Macintyre, Geoff; Demeulemeester, Jonas; Vázquez-García, Ignacio; Kleinheinz, Kortine; Livitz, Dimitri; Malikić, Salem; Donmez, Nilgun; Sengupta, Subhajit; Anur, Pavana; Jolly, Clemency; Cmero, Marek; Rosebrock, Daniel; Schumacher, Steven E.; Fan, Yu; Fittall, Matthew; Drews, Ruben M.; Yao, Xiaotong; Schlesner, Matthias; Zhu, Hongtu; Adams, David J.; Getz, Gad; Boutros, Paul C.; Imieliński, Marcin; Beroukhim, Rameen; Şahinalp, S. Cenk; Ji, Yuan; Peifer, Martin; Markowetz, Florian; Mustonen, Ville; Yuan, Ke; Gerstung, Moritz; Spellman, Paul T.; Wang, Wenyi; Morris, Quaid; Wedge, David C.; Loo, Peter Van

doi:10.1101/312041

Cited by 99 publications

(223 citation statements)

References 64 publications

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“…Genomic data including mutations and copy number variations as well as clinical data were downloaded from 42 . WGD status was calculated using the methods described previously 62 . The amended Inception-V4 architecture, preprocessing scripts and the retrained model checkpoint can be found on our Github repository.…”

Section: Author Contributionsmentioning

confidence: 99%

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Jung

Torné

et al. 2019

Preprint

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Key points• Pan-cancer computational histopathology analysis with deep learning extracts histopathological patterns and accurately discriminates 28 cancer and 14 normal tissue types • Computational histopathology predicts whole genome duplications, focal amplifications and deletions, as well as driver gene mutations • Wide-spread correlations with gene expression indicative of immune infiltration and proliferation • Prognostic information augments conventional grading and histopathology subtyping in the majority of cancers AbstractHere we use deep transfer learning to quantify histopathological patterns across 17,396 H&E stained histopathology image slides from 28 cancer types and correlate these with underlying genomic and transcriptomic data. Pan-cancer computational histopathology (PC-CHiP) classifies the tissue origin across organ sites and provides highly accurate, spatially resolved tumor and normal distinction within a given slide. The learned computational histopathological features correlate with a large range of recurrent genetic aberrations, including whole genome duplications (WGDs), arm-level copy number gains and losses, focal amplifications and deletions as well as driver gene mutations within a range of cancer types. WGDs can be predicted in 25/27 cancer types (mean AUC=0.79) including those that were not part of model training. Similarly, we observe associations with 25% of mRNA transcript levels, which enables to learn and localise histopathological patterns of molecularly defined cell types on each slide. Lastly, we find that computational histopathology provides prognostic information augmenting histopathological subtyping and grading in the majority of cancers assessed, which pinpoints prognostically relevant areas such as necrosis or infiltrating lymphocytes on each tumour section. Taken together, these findings highlight the large potential of PC-CHiP to discover new molecular and prognostic associations, which can augment diagnostic workflows and lay out a rationale for integrating molecular and histopathological data.

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Section: Author Contributionsmentioning

confidence: 99%

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Jung

Torné

et al. 2019

Preprint

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109

166

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“…Species with polymerases that are more closely related would have more similar patterns than those from less related species. Finally, a coupling between metabolism and COSMIC 5-like mutational patterns could help explain why the extent of that type of mutagenesis can be highly variable in cancers, when comparing between different patients ( 12 ) or temporally within the same patient ( 25 ): namely, such variability could be linked to differences in energy metabolism between individuals or over the course of disease progression in particular individuals.…”

Section: Figurementioning

confidence: 99%

Intrinsic base substitution patterns in diverse species reveal links to cancer and metabolism

Alasmar

et al. 2019

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1Mutations in genomic DNA are the starting material for evolution by natural 2 selection. Analyses of large-scale sequencing data have revealed that mu-3 tagenic processes often create distinctive patterns of base substitutions, 4 called mutational signatures. In this work, we analyzed the base substitu-5 tion patterns within mutational data from a large number of strains or indi-6 viduals among seven model species (totalling >784 million mutations), as 7 well as single nucleotide polymorphisms (SNPs) in 41 species from the 8 NCBI SNP database (>603 million SNPs). We found that the intrinsic base 9 substitution pattern for most of these species closely matches mutational 10 signature 5 from the Catalog of Somatic Mutations in Cancer (abbreviated 11 as COSMIC). Signature 5 is ubiquitous in cancers and normal human cells, 12suggesting that the similar patterns of mutation across many species are 13 likely due to conserved biochemical processes. Finally, we show that a 14 similar pattern of base substitutions can be obtained using a yeast model 15 system that allows controlled generation of genomic single-stranded DNA. 16Taken together, we propose that intrinsic biochemical processes in cells 17 are coupled to the continuous generation of mutations, which in turn, are 18 acted upon by natural selection to drive the evolution of species. 19 20

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“…If we were able to follow this progression route with some level of certainty and understand how these trajectories control the fate of the developing clone, this could lead to improved methods for outcome prediction, new early detection and cancer prevention approaches, as well as inform treatment decisions. Recent methodological developments allow exploration of these types of questions using sequencing data to a level of detail not available previously 6,[7][8][9][10][11]12,13 . Both our and others work 6,7,5,11,14,15,16,17,18,19,20,21 explored individual aspects of subclonal diversification and tumour progression in several different tumour types [5][6][7]9,22,23 and developed methods to computationally reconstruct absolute copy number 24,25 , subclonal structure of cell populations 5,26,21 and population structure changes between samples 27,28,29,5,26,6 .…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment

Leshchiner

Livitz

et al. 2018

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Driver mutations alter cells from normal to cancer through several evolutionary epochs: premalignancy, early malignancy, subclonal diversification, metastasis and resistance to therapy. Later stages of disease can be explored through analyzing multiple samples collected longitudinally, on or between successive treatments, and finally at time of autopsy. It is also possible to study earlier stages of cancer development through probabilistic reconstruction of developmental trajectories based on mutational information preserved in the genome. Here we present a suite of tools, called Phylogic N-Dimensional with Timing (PhylogicNDT), that statistically model phylogenetic and evolutionary trajectories based on mutation and copy-number data representing samples taken at single or multiple time points. PhylogicNDT can be used to infer: (i) the order of clonal driver events (including in pre-cancerous stages); (ii) subclonal populations of cells and their phylogenetic relationships; and (iii) cell population dynamics. We demonstrate the use of PhylogicNDT by applying it to whole-exome and whole-genome data of 498 lung adenocarcinoma samples (434 previously available and 64 of newly generated data). We identify significantly different progression trajectories across subtypes of lung adenocarcinoma (EGFR mutant, KRAS mutant, fusion-driven and EGFR/KRAS wild type cancers). In addition, we study the progression of fusiondriven lung cancer in 21 patients by analyzing samples from multiple timepoints during treatment with 1st and next generation tyrosine kinase inhibitors. We characterize their subclonal diversification, dynamics, selection, and changes in mutational signatures and neoantigen load. This methodology will enable a systematic study of tumour initiation, progression and resistance across cancer types and therapies.

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Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes

Cited by 99 publications

References 64 publications

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Intrinsic base substitution patterns in diverse species reveal links to cancer and metabolism

Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment

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