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

Abstract

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.

Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers

“…The data for this study consisted of multiple samples collected from each patient (pre-and post-treatment) as well as autopsy samples. Phylogenetic analysis, subclonal reconstruction and tree building was done with the PhylogicNDT package 36 .…”

“…We analyzed WES data from pre-treatment and post-progression cfDNA, post-progression biopsy, and 17 autopsy specimens (Figs. 3b-d; Supplementary Table 5), and studied the clones and their phylogenetic structure with PhylogicNDT 36 . Interestingly, FGFR2 V564L…”

Growth dynamics in naturally progressing chronic lymphocytic leukaemia

“…having CCFs of ≥ 10% at one or more timepoints) to derive likely phylogenies for each patient (Methods, Supplementary Table 10, Extended Data Figures 5–7). Using PhylogicNDT 18 , we identified subclones by n -dimensional clustering of CCFs of individual events across samples, estimated their phylogenetic relationships and modelled the growth dynamics of each subclone (Methods). We focused on the 5 patients with WES from 4 or more pre-treatment time points (Patients 1, 4, 5, 18, and 19).…”

“…We focused this analysis on the 35 subclones detected in 16 patients whose leukaemias displayed overall non-logistic WBC expansion (since those with overall LOG growth lacked abundant drivers). We inferred the distribution of growth rates of individual subclones and the differences to their parents using an MCMC algorithm that samples an ensemble of likely phylogenetic trees for each patient ( PhylogicNDT GrowthKinetics 18 ; Methods). The model also takes into account the reads supporting each somatic mutation, tumour purity, absolute copy-number, and the WBC measurements (Extended Data Figure 5d).…”

Clonal dynamics in chronic lymphocytic leukemia

“…Recent studies have demonstrated that the analysis of serial samples from the same patient can greatly increase the sensitivity and confidence in the detection of subclonal mutations. 1,27 CLL heterogeneity is not restricted to somatic mutations. Epigenetic mechanisms, such as DNA methylation, chromatin remodeling, and posttranslational histone modification, also contribute to tumor diversity and play a role in cancer evolution.…”