CNETML: Maximum likelihood inference of phylogeny from copy number profiles of spatio-temporal samples

Abstract

Phylogenetic trees based on copy number alterations (CNAs) for multi-region samples of a single cancer patient are helpful to understand the spatio-temporal evolution of cancers, especially in tumours driven by chromosomal instability. Due to the high cost of deep sequencing data, low-coverage data are more accessible in practice, which only allow the calling of (relative) total copy numbers due to the lower resolution. However, methods to reconstruct sample phylogenies from CNAs often use allele-specific copy numbers and those using total copy number are mostly distance matrix or maximum parsimony methods which do not handle temporal data or estimate mutation rates. In this work, we developed a new maximum likelihood method based on a novel evolutionary model of CNAs, CNETML, to infer phylogenies from spatio-temporal samples taken within a single patient. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers when samples were taken at different time points. Our extensive simulations suggest CNETML performed well even on relative copy numbers with subclonal whole genome doubling events and under slight violation of model assumptions. Theapplication of CNETML to real data from Barrett's esophagus patients also generated consistent results with previous discoveries and novel early CNAs for further investigations.

The genomic trajectory of ovarian high grade serous carcinoma is determined in STIC lesions

“…The conventional approach for analysing phylogenetic trees typically relies on somatic mutations or allele-specific copy numbers. However, due to the limited sample input, we applied CNETML [31], a new maximum likelihood method than can infer phylogenetic trees from relative copy number called from sWGS data, to determine the evolutionary trajectory of lesions for each patient (see Supplementary Methods). To visualise the evolutionary process of HGSC better, each phylogenetic tree was transformed into a hierarchy graph based on the inferred tree topology (Supplemental Fig.…”

The genomic trajectory of ovarian high‐grade serous carcinoma can be observed in STIC lesions

“…The conventional approach to analysing phylogenetic trees typically relies on somatic mutations or allele‐specific copy numbers. However, due to the limited sample input, we applied CNETML [30], a new maximum likelihood method than can infer phylogenetic trees from relative copy number called from sWGS data, to determine the evolutionary trajectory of lesions for each patient (see Supplementary materials and methods). To visualise the evolutionary process of HGSC better, each phylogenetic tree was transformed into a hierarchy graph based on the inferred tree topology (supplementary material, Figure S8) and reconstructed ancestral copy numbers from known information on HGSC development and the quality of detected copy numbers (supplementary material, Table S6).…”

CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples

“…CNETML and CNETS are freely available under GPLv3 license at Github [73] and Zenodo [74]. The simulated and processed real data used for generating results are available at Zenodo [75]. The original real data in [13] are available at https:// www.…”