scholarly journals Non-genetic intra-tumor heterogeneity is a major predictor of phenotypic heterogeneity and ongoing evolutionary dynamics in lung tumors

2019 ◽  
Author(s):  
Anchal Sharma ◽  
Elise Merritt ◽  
Xiaoju Hu ◽  
Angelique Cruz ◽  
Chuan Jiang ◽  
...  
Keyword(s):  
Genetic Heterogeneity ◽  
Regional Differences ◽  
Tumor Heterogeneity ◽  
Evolutionary Dynamics ◽  
Lung Squamous Cell Carcinoma ◽  
Purifying Selection ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Major Predictor

ABSTRACTImpacts of genetic and non-genetic intra-tumor heterogeneity (ITH) on tumor phenotypes and evolvability remain debated. We analyzed ITH in lung squamous cell carcinoma (LUSC) at the levels of genome, transcriptome, tumor-immune interactions, and histopathological characteristics by multi-region profiling and using single-cell sequencing data. Overall, in LUSC genomic heterogeneity alone was a weak indicator of intra-tumor non-genetic heterogeneity at immune and transcriptomic levels that impacted multiple cancer-related pathways including those related to proliferation and inflammation, which in turn contributed to intra-tumor regional differences in histopathology and subtype classification. Genome, transcriptome, and immune-level heterogeneity influenced different aspects of tumor evolution. Tumor subclones had substantial differences in proliferation score, suggestive of non-neutral clonal dynamics. Scores for proliferation and other cancer-related pathways also showed intra-tumor regional differences, sometimes even within the same subclones. Neo-epitope burden negatively correlated with immune infiltration, indicating potential immune-mediated purifying selection on acquired mutations in these tumors. Taken together, our observations suggest that non-genetic heterogeneity is a major determinant of heterogeneity in histopathological characteristics and impacts evolutionary dynamics in lung cancer.

scholarly journals Decomposing the subclonal structure of tumors with two-way mixture models on copy number aberrations

2018 ◽  
Author(s):  
An-Shun Tai ◽  
Chien-Hua Peng ◽  
Shih-Chi Peng ◽  
Wen-Ping Hsieh
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Copy Number ◽  
Tumor Heterogeneity ◽  
Tumor Evolution ◽  
Depth Information ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Copy Number Aberrations

AbstractMultistage tumorigenesis is a dynamic process characterized by the accumulation of mutations. Thus, a tumor mass is composed of genetically divergent cell subclones. With the advancement of next-generation sequencing (NGS), mathematical models have been recently developed to decompose tumor subclonal architecture from a collective genome sequencing data. Most of the methods focused on single-nucleotide variants (SNVs). However, somatic copy number aberrations (CNAs) also play critical roles in carcinogenesis. Therefore, further modeling subclonal CNAs composition would hold the promise to improve the analysis of tumor heterogeneity and cancer evolution. To address this issue, we developed a two-way mixture Poisson model, named CloneDeMix for the deconvolution of read-depth information. It can infer the subclonal copy number, mutational cellular prevalence (MCP), subclone composition, and the order in which mutations occurred in the evolutionary hierarchy. The performance of CloneDeMix was systematically assessed in simulations. As a result, the accuracy of CNA inference was nearly 93% and the MCP was also accurately restored. Furthermore, we also demonstrated its applicability using head and neck cancer samples from TCGA. Our results inform about the extent of subclonal CNA diversity, and a group of candidate genes that probably initiate lymph node metastasis during tumor evolution was also discovered. Most importantly, these driver genes are located at 11q13.3 which is highly susceptible to copy number change in head and neck cancer genomes. This study successfully estimates subclonal CNAs and exhibit the evolutionary relationships of mutation events. By doing so, we can track tumor heterogeneity and identify crucial mutations during evolution process. Hence, it facilitates not only understanding the cancer development but finding potential therapeutic targets. Briefly, this framework has implications for improved modeling of tumor evolution and the importance of inclusion of subclonal CNAs.

scholarly journals Divergent and convergent evolution of housekeeping genes in human–pig lineage

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4840 ◽  
Author(s):  
Kai Wei ◽  
Tingting Zhang ◽  
Lei Ma
Keyword(s):  
Active Sites ◽  
Evolutionary Dynamics ◽  
Purifying Selection ◽  
Housekeeping Genes ◽  
Neutral Evolution ◽  
Structure Evolution ◽  
Tissue Cell ◽  
Sequencing Data ◽  
Cellular Functions ◽  
Species Specific

Housekeeping genes are ubiquitously expressed and maintain basic cellular functions across tissue/cell type conditions. The present study aimed to develop a set of pig housekeeping genes and compare the structure, evolution and function of housekeeping genes in the human–pig lineage. By using RNA sequencing data, we identified 3,136 pig housekeeping genes. Compared with human housekeeping genes, we found that pig housekeeping genes were longer and subjected to slightly weaker purifying selection pressure and faster neutral evolution. Common housekeeping genes, shared by the two species, achieve stronger purifying selection than species-specific genes. However, pig- and human-specific housekeeping genes have similar functions. Some species-specific housekeeping genes have evolved independently to form similar protein active sites or structure, such as the classical catalytic serine–histidine–aspartate triad, implying that they have converged for maintaining the basic cellular function, which allows them to adapt to the environment. Human and pig housekeeping genes have varied structures and gene lists, but they have converged to maintain basic cellular functions essential for the existence of a cell, regardless of its specific role in the species. The results of our study shed light on the evolutionary dynamics of housekeeping 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 Non-Genetic Intra-Tumor Heterogeneity Is a Major Predictor of Phenotypic Heterogeneity and Ongoing Evolutionary Dynamics in Lung Tumors

Cell Reports ◽  
2019 ◽  
Vol 29 (8) ◽  
pp. 2164-2174.e5 ◽  
Author(s):  
Anchal Sharma ◽  
Elise Merritt ◽  
Xiaoju Hu ◽  
Angelique Cruz ◽  
Chuan Jiang ◽  
...  
Keyword(s):  
Tumor Heterogeneity ◽  
Evolutionary Dynamics ◽  
Lung Tumors ◽  
Phenotypic Heterogeneity ◽  
Major Predictor

scholarly journals Inference of clonal selection in cancer populations using single-cell sequencing data

2018 ◽  
Author(s):  
Pavel Skums ◽  
Vyacheslau Tsivina ◽  
Alex Zelikovsky
Keyword(s):  
Single Cell ◽  
Cancer Progression ◽  
Tumor Heterogeneity ◽  
Evolutionary Dynamics ◽  
Clonal Selection ◽  
Fitness Landscapes ◽  
Sequencing Data ◽  
Evolutionary Mechanisms ◽  
Single Cell Sequencing ◽  
Insight Into

AbstractIntra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single cell sequencing, which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here we present SCIFIL, a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from single cell sequencing data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy and utility of our approach on simulated and experimental data. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer. Its source code is available at https://github.com/compbel/SCIFIL

scholarly journals DeCiFering the Elusive Cancer Cell Fraction in Tumor Heterogeneity and Evolution

2021 ◽  
Author(s):  
Gryte Satas ◽  
Simone Zaccaria ◽  
Mohammed El-Kebir ◽  
Benjamin J. Raphael
Keyword(s):  
Phylogenetic Analysis ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Copy Number ◽  
Tumor Heterogeneity ◽  
Cell Fraction ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Cancer Cell Fraction

AbstractMost tumors are heterogeneous mixtures of normal cells and cancer cells, with individual cancer cells distinguished by somatic mutations that accumulated during the evolution of the tumor. The fundamental quantity used to measure tumor heterogeneity from somatic single-nucleotide variants (SNVs) is the Cancer Cell Fraction (CCF), or proportion of cancer cells that contain the SNV. However, in tumors containing copy-number aberrations (CNAs) – e.g. most solid tumors – the estimation of CCFs from DNA sequencing data is challenging because a CNA may alter the mutation multiplicity, or number of copies of an SNV. Existing methods to estimate CCFs rely on the restrictive Constant Mutation Multiplicity (CMM) assumption that the mutation multiplicity is constant across all tumor cells containing the mutation. However, the CMM assumption is commonly violated in tumors containing CNAs, and thus CCFs computed under the CMM assumption may yield unrealistic conclusions about tumor heterogeneity and evolution. The CCF also has a second limitation for phylogenetic analysis: the CCF measures the presence of a mutation at the present time, but SNVs may be lost during the evolution of a tumor due to deletions of chromosomal segments. Thus, SNVs that co-occur on the same phylogenetic branch may have different CCFs.In this work, we address these limitations of the CCF in two ways. First, we show how to compute the CCF of an SNV under a less restrictive and more realistic assumption called the Single Split Copy Number (SSCN) assumption. Second, we introduce a novel statistic, the descendant cell fraction (DCF), that quantifies both the prevalence of an SNV and the past evolutionary history of SNVs under an evolutionary model that allows for mutation losses. That is, SNVs that co-occur on the same phylogenetic branch will have the same DCF. We implement these ideas in an algorithm named DeCiFer. DeCiFer computes the DCFs of SNVs from read counts and copy-number proportions and also infers clusters of mutations that are suitable for phylogenetic analysis. We show that DeCiFer clusters SNVs more accurately than existing methods on simulated data containing mutation losses. We apply DeCiFer to sequencing data from 49 metastatic prostate cancer samples and show that DeCiFer produces more parsimonious and reasonable reconstructions of tumor evolution compared to previous approaches. Thus, DeCiFer enables more accurate quantification of intra-tumor heterogeneity and improves downstream inference of tumor evolution.Code availabilitySoftware is available at https://github.com/raphael-group/decifer

scholarly journals Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes

2018 ◽  
Author(s):  
Stefan C. Dentro ◽  
Ignaty Leshchiner ◽  
Kerstin Haase ◽  
Maxime Tarabichi ◽  
Jeff Wintersinger ◽  
...  
Keyword(s):  
Tumor Heterogeneity ◽  
Human Cancer ◽  
Driver Mutations ◽  
Whole Genome Sequencing Data ◽  
Tumor Evolution ◽  
Driver Gene ◽  
Cancer Type ◽  
Whole Genome ◽  
Sequencing Data ◽  
Cancer Types

SUMMARYIntra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin and drivers of ITH across cancer types are poorly understood. To address this question, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples, spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions, with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types, and identify cancer type specific subclonal patterns of driver gene mutations, fusions, structural variants and copy-number alterations, as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution, and provide an unprecedented pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

scholarly journals Inference of clonal selection in cancer populations using single-cell sequencing data

2019 ◽  
Vol 35 (14) ◽  
pp. i398-i407 ◽  
Author(s):  
Pavel Skums ◽  
Viachaslau Tsyvina ◽  
Alex Zelikovsky
Keyword(s):  
Single Cell ◽  
Cancer Progression ◽  
Tumor Heterogeneity ◽  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Clonal Selection ◽  
Fitness Landscapes ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Insight Into

Abstract Summary Intra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single-cell sequencing (scSeq), which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here, we present Single Cell Inference of FItness Landscape (SCIFIL), a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from scSeq data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy our approach, and show how it could be applied to experimental tumor data to study clonal selection and infer evolutionary history. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer. Availability and implementation Its source code is available at https://github.com/compbel/SCIFIL.

scholarly journals HEDGEHOG/GLI Modulates the PRR11-SKA2 Bidirectional Transcription Unit in Lung Squamous Cell Carcinomas

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 120
Author(s):  
Yiyun Sun ◽  
Dandan Xu ◽  
Chundong Zhang ◽  
Yitao Wang ◽  
Lian Zhang ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Squamous Cell ◽  
Gene Pair ◽  
Gene List ◽  
Ectopic Expression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Gene Set

We previously demonstrated that proline-rich protein 11 (PRR11) and spindle and kinetochore associated 2 (SKA2) constituted a head-to-head gene pair driven by a prototypical bidirectional promoter. This gene pair synergistically promoted the development of non-small cell lung cancer. However, the signaling pathways leading to the ectopic expression of this gene pair remains obscure. In the present study, we first analyzed the lung squamous cell carcinoma (LSCC) relevant RNA sequencing data from The Cancer Genome Atlas (TCGA) database using the correlation analysis of gene expression and gene set enrichment analysis (GSEA), which revealed that the PRR11-SKA2 correlated gene list highly resembled the Hedgehog (Hh) pathway activation-related gene set. Subsequently, GLI1/2 inhibitor GANT-61 or GLI1/2-siRNA inhibited the Hh pathway of LSCC cells, concomitantly decreasing the expression levels of PRR11 and SKA2. Furthermore, the mRNA expression profile of LSCC cells treated with GANT-61 was detected using RNA sequencing, displaying 397 differentially expressed genes (203 upregulated genes and 194 downregulated genes). Out of them, one gene set, including BIRC5, NCAPG, CCNB2, and BUB1, was involved in cell division and interacted with both PRR11 and SKA2. These genes were verified as the downregulated genes via RT-PCR and their high expression significantly correlated with the shorter overall survival of LSCC patients. Taken together, our results indicate that GLI1/2 mediates the expression of the PRR11-SKA2-centric gene set that serves as an unfavorable prognostic indicator for LSCC patients, potentializing new combinatorial diagnostic and therapeutic strategies in LSCC.

scholarly journals Human-lineage-specific genomic elements are associated with neurodegenerative disease and APOE transcript usage

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhongbo Chen ◽  
◽  
David Zhang ◽  
Regina H. Reynolds ◽  
Emil K. Gustavsson ◽  
...  
Keyword(s):  
Neurological Diseases ◽  
Purifying Selection ◽  
Whole Genome Sequencing Data ◽  
Human Lineage ◽  
Sequencing Data ◽  
Protein Coding ◽  
Potential Association ◽  
High Depth ◽  
Specific Sequences ◽  
Human Specific

AbstractKnowledge of genomic features specific to the human lineage may provide insights into brain-related diseases. We leverage high-depth whole genome sequencing data to generate a combined annotation identifying regions simultaneously depleted for genetic variation (constrained regions) and poorly conserved across primates. We propose that these constrained, non-conserved regions (CNCRs) have been subject to human-specific purifying selection and are enriched for brain-specific elements. We find that CNCRs are depleted from protein-coding genes but enriched within lncRNAs. We demonstrate that per-SNP heritability of a range of brain-relevant phenotypes are enriched within CNCRs. We find that genes implicated in neurological diseases have high CNCR density, including APOE, highlighting an unannotated intron-3 retention event. Using human brain RNA-sequencing data, we show the intron-3-retaining transcript to be more abundant in Alzheimer’s disease with more severe tau and amyloid pathological burden. Thus, we demonstrate potential association of human-lineage-specific sequences in brain development and neurological disease.

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