scholarly journals Detecting repeated cancer evolution in human tumours from multi-region sequencing data

2017 ◽  
Author(s):  
Giulio Caravagna ◽  
Ylenia Giarratano ◽  
Daniele Ramazzotti ◽  
Trevor A Graham ◽  
Guido Sanguinetti ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Phylogenetic Trees ◽  
Evolutionary Process ◽  
Cancer Evolution ◽  
Sequencing Data ◽  
Robust Identification ◽  
Genomic Changes ◽  
Evolutionary Trajectories ◽  
Genomic Aberrations ◽  
Repeated Evolution

AbstractCarcinogenesis is an evolutionary process driven by the accumulation of genomic aberrations. Recurrent sequences of genomic changes, both between and within patients, reflect repeated evolution that is valuable for anticipating cancer progression. Multi-region sequencing and phylogenetic analysis allow inference of the partial temporal order of genomic changes within a patient’s tumour. However, the inherent stochasticity of the evolutionary process makes phylogenetic trees from different patients appear very distinct, preventing the robust identification of recurrent evolutionary trajectories. Here we present a novel quantitative method based on a machine learning approach called Transfer Learning (TL) that allows overcoming the stochastic effects of cancer evolution and highlighting hidden recurrences in cancer patient cohorts. When applied to multi-region sequencing datasets from lung, breast and renal cancer (708 samples from 160 patients), our method detected repeated evolutionary trajectories that determine novel patient subgroups, which reproduce in large singlesample cohorts (n=2,641) and have prognostic value. Our method provides a novel patient classification measure that is grounded in the cancer evolution paradigm, and which reveals repeated evolution during tumorigenesis, with implications for our ability to anticipate malignant evolution.

scholarly journals Molecular Biology and Evolution of Cancer: From Discovery to Action

2019 ◽  
Vol 37 (2) ◽  
pp. 320-326 ◽  
Author(s):  
Jason A Somarelli ◽  
Heather Gardner ◽  
Vincent L Cannataro ◽  
Ella F Gunady ◽  
Amy M Boddy ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Cancer Biology ◽  
Circulatory System ◽  
Evolutionary Process ◽  
Deep Understanding ◽  
The Body ◽  
Ecological Niches ◽  
Cell Populations ◽  
Cancer Evolution ◽  
Metastatic Sites

Abstract Cancer progression is an evolutionary process. During this process, evolving cancer cell populations encounter restrictive ecological niches within the body, such as the primary tumor, circulatory system, and diverse metastatic sites. Efforts to prevent or delay cancer evolution—and progression—require a deep understanding of the underlying molecular evolutionary processes. Herein we discuss a suite of concepts and tools from evolutionary and ecological theory that can inform cancer biology in new and meaningful ways. We also highlight current challenges to applying these concepts, and propose ways in which incorporating these concepts could identify new therapeutic modes and vulnerabilities in cancer.

scholarly journals On measuring selection in cancer from subclonal mutation frequencies

2019 ◽  
Author(s):  
Ivana Bozic ◽  
Chay Paterson ◽  
Bartlomiej Waclaw
Keyword(s):  
Cancer Progression ◽  
Treatment Strategies ◽  
Intermediate Frequency ◽  
Neutral Evolution ◽  
Relevant Parameter ◽  
Cancer Evolution ◽  
Sequencing Data ◽  
Novel Treatment Strategies ◽  
Subclonal Mutation ◽  
Expanding Population

ABSTRACTRecently available cancer sequencing data have revealed a complex view of the cancer genome containing a multitude of mutations, including drivers responsible for cancer progression and neutral passengers. Measuring selection in cancer and distinguishing drivers from passengers have important implications for development of novel treatment strategies. It has recently been argued that a third of cancers are evolving neutrally, as their mutational frequency spectrum follows a 1/f power law expected from neutral evolution in a particular intermediate frequency range. We study a stochastic model of cancer evolution and derive a formula for the probability distribution of the cancer cell frequency of a subclonal driver, demonstrating that driver frequency is biased towards 0 and 1. We show that it is difficult to capture a driver mutation at an intermediate frequency, and thus the calling of neutrality due to a lack of such driver will significantly overestimate the number of neutrally evolving tumors. Our approach provides precise quantification of the validity of the 1/f statistic across the entire range of all relevant parameter values. Our results are also applicable to the question of distinguishing driver and passenger mutations in a general exponentially expanding population.

scholarly journals Estimating the predictability of cancer evolution

2019 ◽  
Vol 35 (14) ◽  
pp. i389-i397 ◽  
Author(s):  
Sayed-Rzgar Hosseini ◽  
Ramon Diaz-Uriarte ◽  
Florian Markowetz ◽  
Niko Beerenwinkel
Keyword(s):  
Cancer Progression ◽  
Fitness Landscape ◽  
Simulated Data ◽  
Fitness Landscapes ◽  
Computational Method ◽  
Supplementary Information ◽  
Cancer Evolution ◽  
Statistical Framework ◽  
Evolutionary Trajectories

Abstract Motivation How predictable is the evolution of cancer? This fundamental question is of immense relevance for the diagnosis, prognosis and treatment of cancer. Evolutionary biologists have approached the question of predictability based on the underlying fitness landscape. However, empirical fitness landscapes of tumor cells are impossible to determine in vivo. Thus, in order to quantify the predictability of cancer evolution, alternative approaches are required that circumvent the need for fitness landscapes. Results We developed a computational method based on conjunctive Bayesian networks (CBNs) to quantify the predictability of cancer evolution directly from mutational data, without the need for measuring or estimating fitness. Using simulated data derived from >200 different fitness landscapes, we show that our CBN-based notion of evolutionary predictability strongly correlates with the classical notion of predictability based on fitness landscapes under the strong selection weak mutation assumption. The statistical framework enables robust and scalable quantification of evolutionary predictability. We applied our approach to driver mutation data from the TCGA and the MSK-IMPACT clinical cohorts to systematically compare the predictability of 15 different cancer types. We found that cancer evolution is remarkably predictable as only a small fraction of evolutionary trajectories are feasible during cancer progression. Availability and implementation https://github.com/cbg-ethz/predictability\_of\_cancer\_evolution Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Early and multiple origins of metastatic lineages within primary tumors

2016 ◽  
Vol 113 (8) ◽  
pp. 2140-2145 ◽  
Author(s):  
Zi-Ming Zhao ◽  
Bixiao Zhao ◽  
Yalai Bai ◽  
Atila Iamarino ◽  
Stephen G. Gaffney ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Cancer Biology ◽  
Tumor Development ◽  
Evolutionary Process ◽  
Driver Mutations ◽  
Cancer Evolution ◽  
Driver Genes ◽  
Genetic Changes ◽  
Primary Tumors ◽  
Multiple Origins

Many aspects of the evolutionary process of tumorigenesis that are fundamental to cancer biology and targeted treatment have been challenging to reveal, such as the divergence times and genetic clonality of metastatic lineages. To address these challenges, we performed tumor phylogenetics using molecular evolutionary models, reconstructed ancestral states of somatic mutations, and inferred cancer chronograms to yield three conclusions. First, in contrast to a linear model of cancer progression, metastases can originate from divergent lineages within primary tumors. Evolved genetic changes in cancer lineages likely affect only the proclivity toward metastasis. Single genetic changes are unlikely to be necessary or sufficient for metastasis. Second, metastatic lineages can arise early in tumor development, sometimes long before diagnosis. The early genetic divergence of some metastatic lineages directs attention toward research on driver genes that are mutated early in cancer evolution. Last, the temporal order of occurrence of driver mutations can be inferred from phylogenetic analysis of cancer chronograms, guiding development of targeted therapeutics effective against primary tumors and metastases.

scholarly journals Tumor subclonal progression model for cancer hallmark acquisition

2017 ◽  
Author(s):  
Yusuke Matsui ◽  
Satoru Miyano ◽  
Teppei Shimamura
Keyword(s):  
Large Scale ◽  
Clear Cell ◽  
Genomic Data ◽  
Evolutionary Tree ◽  
Cancer Evolution ◽  
Sequencing Data ◽  
Progression Model ◽  
Important Challenge ◽  
Evolutionary Trajectories ◽  
Novel Method

AbstractRecent advances in the methods for reconstruction of cancer evolutionary trajectories opened up the prospects of deciphering the subclonal populations and their evolutionary architectures within cancer ecosystems. An important challenge of the cancer evolution studies is how to connect genetic aberrations in subclones to a clinically interpretable and actionable target in the subclones for individual patients. In this study, our aim is to develop a novel method for constructing a model of tumor subclonal progression in terms of cancer hallmark acquisition using multiregional sequencing data. We prepare a subclonal evolutionary tree inferred from variant allele frequencies and estimate pathway alteration probabilities from large-scale cohort genomic data. We then construct an evolutionary tree of pathway alterations that takes into account selectivity of pathway alterations via selectivity score. We show the effectiveness of our method on a dataset of clear cell renal cell carcinomas.

scholarly journals Detecting evolutionary patterns of cancers using consensus trees

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i684-i691
Author(s):  
Sarah Christensen ◽  
Juho Kim ◽  
Nicholas Chia ◽  
Oluwasanmi Koyejo ◽  
Mohammed El-Kebir
Keyword(s):  
Evolutionary Process ◽  
Simulated Data ◽  
Therapy Response ◽  
Supplementary Information ◽  
Driver Mutations ◽  
Sequencing Data ◽  
Large Solution ◽  
Evolutionary Patterns ◽  
Cancer Subtypes ◽  
Evolutionary Trajectories

Abstract Motivation While each cancer is the result of an isolated evolutionary process, there are repeated patterns in tumorigenesis defined by recurrent driver mutations and their temporal ordering. Such repeated evolutionary trajectories hold the potential to improve stratification of cancer patients into subtypes with distinct survival and therapy response profiles. However, current cancer phylogeny methods infer large solution spaces of plausible evolutionary histories from the same sequencing data, obfuscating repeated evolutionary patterns. Results To simultaneously resolve ambiguities in sequencing data and identify cancer subtypes, we propose to leverage common patterns of evolution found in patient cohorts. We first formulate the Multiple Choice Consensus Tree problem, which seeks to select a tumor tree for each patient and assign patients into clusters in such a way that maximizes consistency within each cluster of patient trees. We prove that this problem is NP-hard and develop a heuristic algorithm, Revealing Evolutionary Consensus Across Patients (RECAP), to solve this problem in practice. Finally, on simulated data, we show RECAP outperforms existing methods that do not account for patient subtypes. We then use RECAP to resolve ambiguities in patient trees and find repeated evolutionary trajectories in lung and breast cancer cohorts. Availability and implementation https://github.com/elkebir-group/RECAP. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Identification of conserved evolutionary trajectories in tumors

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i427-i435 ◽  
Author(s):  
Ermin Hodzic ◽  
Raunak Shrestha ◽  
Salem Malikic ◽  
Colin C Collins ◽  
Kevin Litchfield ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
Optimization Method ◽  
Supplementary Information ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Cell Renal Cell Carcinoma ◽  
Tumor Biopsy ◽  
Lung Cancer Patients ◽  
History Of ◽  
Evolutionary Trajectories

Abstract Motivation As multi-region, time-series and single-cell sequencing data become more widely available; it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics). Results In this article, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories. Availability and implementation CONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Identification of Conserved Evolutionary Trajectories in Tumors

2020 ◽  
Author(s):  
Ermin Hodzic ◽  
Raunak Shrestha ◽  
Salem Malikic ◽  
Colin C. Collins ◽  
Kevin Litchfield ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
Optimization Method ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Cell Renal Cell Carcinoma ◽  
Tumor Biopsy ◽  
Lung Cancer Patients ◽  
Single Cell Sequencing ◽  
History Of ◽  
Evolutionary Trajectories

AbstractMotivationAs multi-region, time-series, and single cell sequencing data become more widely available, it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics).ResultsIn this paper, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories.AvailabilityCONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT.

scholarly journals Whole-genome doubling-aware copy number phylogenies for cancer evolution with MEDICC2

2021 ◽  
Author(s):  
Marina Petkovic ◽  
Thomas BK Watkins ◽  
Emma C Colliver ◽  
Sofya Laskina ◽  
Charles Swanton ◽  
...  
Keyword(s):  
Copy Number ◽  
Phylogenetic Trees ◽  
Evolutionary Process ◽  
Recent Common Ancestor ◽  
Whole Genome ◽  
Cancer Evolution ◽  
Minimum Evolution ◽  
Genome Doubling ◽  
Most Recent Common Ancestor ◽  
Whole Genome Doubling

Chromosomal instability (CIN) and somatic copy number alterations (SCNA) play a key role in the evolutionary process that shapes cancer genomes. SCNAs comprise many classes of clinically relevant events, such as localised amplifications, gains, losses, loss-of-heterozygosity (LOH) events, and recently discovered parallel evolutionary events revealed by multi-sample phasing. These events frequently appear jointly with whole genome doubling (WGD), a transformative event in tumour evolution, which generates tetraploid or near-tetraploid cells. WGD events are often clonal, occuring before the emergence of the most recent common ancestor, and have been associated with increased CIN, poor patient outcome and are currently being investigated as potential therapeutic targets. While SCNAs can provide a rich source of phylogenetic information, so far no method exists for phylogenetic inference from SCNAs that includes WGD events. Here we present MEDICC2, a new phylogenetic algorithm for allele-specific SCNA data based on a minimum-evolution criterion that explicitly models clonal and subclonal WGD events and that takes parallel evolutionary events into account. MEDICC2 can identify WGD events and quantify SCNA burden in single-sample studies and infer phylogenetic trees and ancestral genomes in multi-sample scenarios. In this scenario, it accurately locates clonal and subclonal WGD events as well as parallel evolutionary events in the evolutionary history of the tumour, timing SCNAs relative to each other. We use MEDICC2 to detect WGD events in 2778 tumours with 98.8% accuracy and show its ability to correctly place subclonal WGD events in simulated and real-world multi-sample tumours, while accurately inferring its phylogeny and parallel SCNA events. MEDICC2 is implemented in Python 3 and freely available under GPLv3 at https://bitbucket.org/schwarzlab/medicc2.

scholarly journals TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data

2015 ◽  
Author(s):  
Luca De Sano ◽  
Giulio Caravagna ◽  
Daniele Ramazzotti ◽  
Alex Graudenzi ◽  
Giancarlo Mauri ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Population Level ◽  
R Package ◽  
Sequencing Data ◽  
Cross Sectional ◽  
Individual Level ◽  
Link Type ◽  
Multiple Biopsies ◽  
Evolutionary Trajectories ◽  
Multiple Samples

AbstractMotivationWe introduce TRONCO (TRanslational ONCOlogy), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract population-level models describing the trends of accumulation of alterations in a cohort of cross-sectional samples, e.g., retrieved from publicly available databases, and individual-level models that reveal the clonal evolutionary history in single cancer patients, when multiple samples, e.g., multiple biopsies or single-cell sequencing data, are available. The resulting models can provide key hints in uncovering the evolutionary trajectories of cancer, especially for precision medicine or personalized therapy.AvailabilityTRONCO is released under the GPL license, it is hosted in the Software section at http://bimib.disco.unimib.it/ and archived also at [email protected]

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