scholarly journals Evolutionary dynamics of neoantigens in growing tumours

2019 ◽  
Author(s):  
Eszter Lakatos ◽  
Marc J. Williams ◽  
Ryan O. Schenck ◽  
William C. H. Cross ◽  
Jacob Househam ◽  
...  
Keyword(s):  
Negative Selection ◽  
Evolutionary Dynamics ◽  
Immune Escape ◽  
Patient Specific ◽  
Mathematical Framework ◽  
Cancer Evolution ◽  
Sequencing Data ◽  
Clone Size ◽  
Strong Negative Selection ◽  
Immune Escape Mechanisms

ABSTRACTCancer evolution is driven by the acquisition of somatic mutations that provide cells with a beneficial phenotype in a changing microenvironment. However, mutations that give rise to neoantigens, novel cancer–specific peptides that elicit an immune response, are likely to be disadvantageous. Here we show how the clonal structure and immunogenotype of growing tumours is shaped by negative selection in response to neoantigenic mutations. We construct a mathematical model of neoantigen evolution in a growing tumour, and verify the model using genomic sequencing data. The model predicts that, in the absence of active immune escape mechanisms, tumours either evolve clonal neoantigens (antigen– ‘hot’), or have no clonally– expanded neoantigens at all (antigen– ‘cold’), whereas antigen– ‘warm’ tumours (with high frequency subclonal neoantigens) form only following the evolution of immune evasion. Counterintuitively, strong negative selection for neoantigens during tumour formation leads to an increased number of antigen– warm or – hot tumours, as a consequence of selective pressure for immune escape. Further, we show that the clone size distribution under negative selection is effectively– neutral, and moreover, that stronger negative selection paradoxically leads to more neutral– like dynamics. Analysis of antigen clone sizes and immune escape in colorectal cancer exome sequencing data confirms these results. Overall, we provide and verify a mathematical framework to understand the evolutionary dynamics and clonality of neoantigens in human cancers that may inform patient– specific immunotherapy decision– making.

Opposing Evolutionary Pressures Drive Clonal Evolution and Health Outcomes in the Aging Blood System

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 37-37
Author(s):  
Kimberly Skead ◽  
Armande Ang Houle ◽  
Sagi Abelson ◽  
Marie-Julie Fave ◽  
Boxi Lin ◽  
...  
Keyword(s):  
Gene Disruption ◽  
Large Scale ◽  
Evolutionary Dynamics ◽  
Clonal Evolution ◽  
Neutral Evolution ◽  
Driver Mutations ◽  
Cancer Evolution ◽  
Sequencing Data ◽  
High Coverage ◽  
Passenger Mutations

The age-associated accumulation of somatic mutations and large-scale structural variants (SVs) in the early hematopoietic hierarchy have been linked to premalignant stages for cancer and cardiovascular disease (CVD). However, only a small proportion of individuals harboring these mutations progress to disease, and mechanisms driving the transformation to malignancy remains unclear. Hematopoietic evolution, and cancer evolution more broadly, has largely been studied through a lens of adaptive evolution and the contribution of functionally neutral or mildly damaging mutations to early disease-associated clonal expansions has not been well characterised despite comprising the majority of the mutational burden in healthy or tumoural tissues. Through combining deep learning with population genetics, we interrogate the hematopoietic system to capture signatures of selection acting in healthy and pre-cancerous blood populations. Here, we leverage high-coverage sequencing data from healthy and pre-cancerous individuals from the European Prospective Investigation into Cancer and Nutrition Study (n=477) and dense genotyping from the Canadian Partnership for Tomorrow's Health (n=5,000) to show that blood rejects the paradigm of strictly adaptive or neutral evolution and is subject to pervasive negative selection. We observe clear age associations across hematopoietic populations and the dominant class of selection driving evolutionary dynamics acting at an individual level. We find that both the location and ratio of passenger to driver mutations are critical in determining if positive selection acting on driver mutations is able to overwhelm regulated hematopoiesis and allow clones harbouring disease-predisposing mutations to rise to dominance. Certain genes are enriched for passenger mutations in healthy individuals fitting purifying models of evolution, suggesting that the presence of passenger mutations in a subset of genes might confer a protective role against disease-predisposing clonal expansions. Finally, we find that the density of gene disruption events with known pathogenic associations in somatic SVs impacts the frequency at which the SV segregates in the population with variants displaying higher gene disruption density segregating at lower frequencies. Understanding how blood evolves towards malignancy will allow us to capture cancer in its earliest stages and identify events initiating departures from healthy blood evolution. Further, as the majority of mutations are passengers, studying their contribution to tumorigenesis, will unveil novel therapeutic targets thus enabling us to better understand patterns of clonal evolution in order to diagnose and treat disease in its infancy. Disclosures Dick: Bristol-Myers Squibb/Celgene: Research Funding.

scholarly journals Creating Standards for Evaluating Tumour Subclonal Reconstruction

2018 ◽  
Author(s):  
Adriana Salcedo ◽  
Maxime Tarabichi ◽  
Shadrielle Melijah G. Espiritu ◽  
Amit G. Deshwar ◽  
Matei David ◽  
...  
Keyword(s):  
Evolutionary Dynamics ◽  
Read Depth ◽  
Response To Therapy ◽  
Systematic Evaluation ◽  
Computational Techniques ◽  
Cancer Evolution ◽  
Sequencing Data ◽  
Somatic Variant ◽  
Quantitative Metrics ◽  
Algorithmic Problems

AbstractTumours evolve through time and space. Computational techniques have been developed to infer their evolutionary dynamics from DNA sequencing data. A growing number of studies have used these approaches to link molecular cancer evolution to clinical progression and response to therapy. There has not yet been a systematic evaluation of methods for reconstructing tumour subclonality, in part due to the underlying mathematical and biological complexity and to difficulties in creating gold-standards. To fill this gap, we systematically elucidated the key algorithmic problems in subclonal reconstruction and developed mathematically valid quantitative metrics for evaluating them. We then created approaches to simulate realistic tumour genomes, harbouring all known mutation types and processes both clonally and subclonally. We then simulated 580 tumour genomes for reconstruction, varying tumour read-depth and benchmarking somatic variant detection and subclonal reconstruction strategies. The inference of tumour phylogenies is rapidly becoming standard practice in cancer genome analysis; this study creates a baseline for its evaluation.

scholarly journals Use of signals of positive and negative selection to distinguish cancer genes and passenger genes

2020 ◽  
Author(s):  
László Bányai ◽  
Mária Trexler ◽  
Krisztina Kerekes ◽  
Orsolya Csuka ◽  
László Patthy
Keyword(s):  
Negative Selection ◽  
Cancer Genomics ◽  
Tumor Evolution ◽  
Cancer Genes ◽  
Cancer Evolution ◽  
Human Genes ◽  
Strong Negative Selection ◽  
Selection For ◽  
Inactivating Mutations

AbstractA major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most approaches focused on genes that are positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. In the present work we have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes. Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations. Oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells.

scholarly journals Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios

2019 ◽  
Author(s):  
Marc J Williams ◽  
Luiz Zapata ◽  
Benjamin Werner ◽  
Chris Barnes ◽  
Andrea Sottoriva ◽  
...  
Keyword(s):  
Evolutionary Dynamics ◽  
Quantitative Model ◽  
Driver Mutations ◽  
Cancer Evolution ◽  
Sequencing Data ◽  
Synonymous Mutations ◽  
Fitness Effects ◽  
Somatic Evolution ◽  
Normal Oesophagus

AbstractThe distribution of fitness effects (DFE) defines how new mutations spread through an evolving population. The ratio of non-synonymous to synonymous mutations (dN/dS) has become a popular method to detect selection in somatic cells, however the link, in somatic evolution, between dN/dS values and fitness coefficients is missing. Here we present a quantitative model of somatic evolutionary dynamics that yields the selective coefficients from individual driver mutations from dN/dS estimates, and then measure the DFE for somatic mutant clones in ostensibly normal oesophagus and skin. We reveal a broad distribution of fitness effects, with the largest fitness increases found for TP53 and NOTCH1 mutants (proliferative bias 1-5%). Accurate measurement of the per-gene DFE in cancer evolution is precluded by the quality of currently available sequencing data. This study provides the theoretical link between dN/dS values and selective coefficients in somatic evolution, and reveals the DFE for mutations in human tissues.

scholarly journals Identification of Genes under Purifying Selection in Human Cancers

2017 ◽  
Author(s):  
Robert A. Mathis ◽  
Ethan S. Sokol ◽  
Piyush B. Gupta
Keyword(s):  
Negative Selection ◽  
Somatic Mutations ◽  
Purifying Selection ◽  
Sequencing Data ◽  
Systematic Assessment ◽  
Coding Regions ◽  
Strong Negative Selection ◽  
Cancer Genomes ◽  
Lung Adenocarcinomas ◽  
Widespread Interest

AbstractThere is widespread interest in finding therapeutic vulnerabilities by analyzing the somatic mutations in cancers. Most analyses have focused on identifying driver oncogenes mutated in patient tumors, but this approach is incapable of discovering genes essential for tumor growth yet not activated through mutation. We show that such genes can be systematically discovered by mining cancer sequencing data for evidence of purifying selection. We show that purifying selection reduces substitution rates in coding regions of cancer genomes, depleting up to 90% of mutations for some genes. Moreover, mutations resulting in non-conservative amino acid substitutions are under strong negative selection in tumors, whereas conservative substitutions are more tolerated. Genes under purifying selection include members of the EGFR and FGFR pathways in lung adenocarcinomas, and DNA repair pathways in melanomas. A systematic assessment of purifying selection in tumors would identify hundreds of tumor-specific enablers and thus novel targets for therapy.

scholarly journals Whole Genome Doubling mitigates Muller’s Ratchet in Cancer Evolution

2019 ◽  
Author(s):  
Saioa López ◽  
Emilia Lim ◽  
Ariana Huebner ◽  
Michelle Dietzen ◽  
Thanos Mourikis ◽  
...  
Keyword(s):  
Negative Selection ◽  
Chromosome Complement ◽  
Essential Genes ◽  
Tumour Suppressor Genes ◽  
Whole Genome ◽  
Cancer Genes ◽  
Cancer Evolution ◽  
Genome Doubling ◽  
Whole Genome Doubling ◽  
Strong Negative Selection

AbstractWhole genome doubling (WGD) is a prevalent macro-evolutionary event in cancer, involving a doubling of the entire chromosome complement. However, despite its prevalence and clinical prognostic relevance, the evolutionary selection pressures for WGD have not been investigated. Here, we explored whether WGD may act to mitigate the irreversible, inexorable ratchet-like, accumulation of deleterious mutations in essential genes. Utilizing 1050 tumor regions from 816 non-small cell lung cancers (NSCLC), we temporally dissect mutations to determine their temporal acquisition in relation to WGD. We find evidence for strong negative selection against homozygous loss of essential cancer genes prior to WGD. However, mutations in essential genes occurring after duplication were not subject to significant negative selection, consistent with WGD providing a buffering effect, decreasing the likelihood of homozygous loss. Finally, we demonstrate that loss of heterozygosity and temporal dissection of mutations can be exploited to identify signals of positive selection in lung, breast, colorectal cancer and other cancer types, enabling the elucidation of novel tumour suppressor genes and a deeper characterization of known cancer genes.

scholarly journals A pan-cancer signature of neutral tumor evolution

2015 ◽  
Author(s):  
Andrea Sottoriva ◽  
Trevor Graham
Keyword(s):  
Power Law ◽  
Large Scale ◽  
Evolutionary Dynamics ◽  
Genomic Data ◽  
Next Generation Sequencing Data ◽  
Patient Specific ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Power Law Distribution ◽  
Multiple Tumor

Despite extraordinary efforts to profile cancer genomes on a large scale, interpreting the vast amount of genomic data in the light of cancer evolution and in a clinically relevant manner remains challenging. Here we demonstrate that cancer next-generation sequencing data is dominated by the signature of growth governed by a power-law distribution of mutant allele frequencies. The power-law signature is common to multiple tumor types and is a consequence of the effectively-neutral evolutionary dynamics that underpin the evolution of a large proportion of cancers, giving rise to the abundance of mutations responsible for intra-tumor heterogeneity. Importantly, the law allows the measurement, in each individual cancer, of the in vivo mutation rate and the timing of mutations with remarkable precision. This result provides a new way to interpret cancer genomic data by considering the physics of tumor growth in a way that is both patient-specific and clinically relevant.

scholarly journals Use of signals of positive and negative selection to distinguish cancer genes and passenger genes

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
László Bányai ◽  
Maria Trexler ◽  
Krisztina Kerekes ◽  
Orsolya Csuka ◽  
László Patthy
Keyword(s):  
Negative Selection ◽  
Cancer Genomics ◽  
Tumor Evolution ◽  
Cancer Genes ◽  
Cancer Evolution ◽  
Human Genes ◽  
Strong Negative Selection ◽  
Selection For ◽  
Inactivating Mutations

A major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most approaches focused on genes positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. We have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes. Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations, oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells.

scholarly journals Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Angela M. Jarrett ◽  
David A. Hormuth ◽  
Vikram Adhikarla ◽  
Prativa Sahoo ◽  
Daniel Abler ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Targeted Therapy ◽  
Treatment Response ◽  
Growth Factor Receptor ◽  
Patient Specific ◽  
Mathematical Framework ◽  
Breast Cancer Patients ◽  
Data Types ◽  
Her2 Breast Cancer ◽  
Mri Scans

AbstractWhile targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.

Measuring evolutionary cancer dynamics from genome sequencing, one patient at a time

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Giulio Caravagna
Keyword(s):  
Genome Sequencing ◽  
Cancer Evolution ◽  
Sequencing Data ◽  
Evolutionary Forces ◽  
Sequencing Technologies ◽  
Cancer Genome Sequencing ◽  
Multiple Resolutions ◽  
Multiple Patients ◽  
Single Tumour ◽  
Generation Sequencing

AbstractCancers progress through the accumulation of somatic mutations which accrue during tumour evolution, allowing some cells to proliferate in an uncontrolled fashion. This growth process is intimately related to latent evolutionary forces moulding the genetic and epigenetic composition of tumour subpopulations. Understanding cancer requires therefore the understanding of these selective pressures. The adoption of widespread next-generation sequencing technologies opens up for the possibility of measuring molecular profiles of cancers at multiple resolutions, across one or multiple patients. In this review we discuss how cancer genome sequencing data from a single tumour can be used to understand these evolutionary forces, overviewing mathematical models and inferential methods adopted in field of Cancer Evolution.

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