Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment
| Autor: | Justin F. Gainor, James W. Rocco, Sai-Hong Ignatius Ou, Aina Zurita Martinez, Mari Mino-Kenudson, Marguerite Rooney, Alice T. Shaw, Mroz E, Catherine J. Wu, Daniel Rosebrock, Jaegil Kim, J. A. Engelman, Dimitri Livitz, Jiachen Lin, Ignaty Leshchiner, Oliver Spiro, Liudmila Elagina, Ivana Bozic, Chip Stewart, Gad Getz |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0303 health sciences
Mutation education.field_of_study Population Cancer Computational biology Biology medicine.disease medicine.disease_cause Malignancy 3. Good health Metastasis 03 medical and health sciences 0302 clinical medicine 030220 oncology & carcinogenesis medicine Adenocarcinoma KRAS Lung cancer education 030304 developmental biology |
| DOI: | 10.1101/508127 |
| Popis: | 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 fusion-driven 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. |
| Databáze: | OpenAIRE |
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