Evolutionary genomics of mammalian lung cancer genes reveals signatures of positive selection in APC, RB1 and TP53

Genomics ◽  
2020 ◽  
Vol 112 (6) ◽  
pp. 4722-4731
Author(s):  
Mohamed Emam ◽  
João Paulo Machado ◽  
Agostinho Antunes
Keyword(s):  
Lung Cancer ◽  
Positive Selection ◽  
Evolutionary Genomics ◽  
Cancer Genes ◽  
Mammalian Lung

Lung cancer risk stratification using methylation profile in the oral epithelium

2018 ◽  
Vol 27 (2) ◽  
pp. 87-92 ◽  
Author(s):  
Hiroaki Harada ◽  
Kazuaki Miyamaoto ◽  
Masami Kimura ◽  
Tetsuro Ishigami ◽  
Kiyomi Taniyama ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dna Methylation ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Lung Cancer Risk ◽  
Cancer Risk Assessment ◽  
Oral Epithelium ◽  
Specific Gene ◽  
Aberrant Methylation ◽  
Cancer Genes

Background Assuming that the entire airway is affected by the same inhaled carcinogen, similar molecular alterations may occur in the lung and oral cavity. Thus, we hypothesized that DNA methylation profiles in the oral epithelium may be a promising biomarker for lung cancer risk stratification. Methods A methylation-specific polymerase chain reaction was performed on oral epithelium from 16 patients with lung cancer and 32 controls without lung cancer. Genes showing aberrant methylation profiles in the oral epithelium were compared between patients and controls. Results The analysis revealed that HOXD11 and PCDHGB6 were methylated more frequently in patients than in controls ( p = 0.0055 and p = 0.0247, respectively). Combined analyses indicated that 8 of 16 (50%) patients and 3 of 32 (9.4%) controls showed DNA methylation in both genes ( p = 0.0016). Among the population limited to current and former smokers, 6 of 11 (54.5%) patients showed methylation in both genes, compared to 1 of 17 (5.9%) controls ( p = 0.0037). In a subgroup analysis limited to the population above 50-years old, 8 of 16 (50%) patients and 2 of 16 (12.5%) controls showed methylation in both genes ( p = 0.0221). Conclusions The results of this study indicate that specific gene methylation in the oral epithelium might be a promising biomarker for lung cancer risk assessment, especially among smokers. Risk stratification through the analysis of DNA methylation profiles in the oral epithelium may be a useful and less invasive first-step approach in an efficient two-step lung cancer screening strategy.

scholarly journals Discovering the drivers of clonal hematopoiesis

2020 ◽  
Author(s):  
Oriol Pich ◽  
Iker Reyes-Salazar ◽  
Abel Gonzalez-Perez ◽  
Nuria Lopez-Bigas
Keyword(s):  
Positive Selection ◽  
Molecular Mechanisms ◽  
Somatic Mutations ◽  
Cancer Genomics ◽  
Variant Calling ◽  
Selective Advantage ◽  
Cancer Genes ◽  
Driver Genes ◽  
Hematopoietic Stem ◽  
Clonal Hematopoiesis

AbstractMutations in genes that confer a selective advantage to hematopoietic stem cells (HSCs) in certain conditions drive clonal hematopoiesis (CH). While some CH drivers have been identified experimentally or through epidemiological studies, the compendium of all genes able to drive CH upon mutations in HSCs is far from complete. We propose that identifying signals of positive selection in blood somatic mutations may be an effective way to identify CH driver genes, similarly as done to identify cancer genes. Using a reverse somatic variant calling approach, we repurposed whole-genome and whole-exome blood/tumor paired samples of more than 12,000 donors from two large cancer genomics cohorts to identify blood somatic mutations. The application of IntOGen, a robust driver discovery pipeline, to blood somatic mutations across both cohorts, and more than 24,000 targeted sequenced samples yielded a list of close to 70 genes with signals of positive selection in CH, available at http://www.intogen.org/ch. This approach recovers all known CH genes, and discovers novel candidates. Generating this compendium is an essential step to understand the molecular mechanisms of CH and to accurately detect individuals with CH to ascertain their risk to develop related diseases.

Engineering the Cancer Genome

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. SCI-19-SCI-19
Author(s):  
Tyler Jacks
Keyword(s):  
Lung Cancer ◽  
Immune Responses ◽  
Tumor Progression ◽  
Nk Cell ◽  
Conflicts Of Interest ◽  
Wnt Signaling Pathway ◽  
Cancer Genome ◽  
Lineage Tracing ◽  
Cancer Genes ◽  
T Cell Antigens

Abstract Over the past several years, my laboratory has employed gene targeting technology to create a series of mouse models of cancer that share genetic and pathological features of the cognate diseases in humans. We have focused particularly on models of lung cancer, both adenocarcinoma and small cell lung cancer. These models have been subjected to various types of molecular characterization, including mRNA profiling and exome/whole-genome sequencing. These efforts have defined genes and pathways that promote tumor progression and metastasis, including in the case of the adenocarcinoma model down-regulation of the lineage-restricted transcription factor Nkx2.1 and other transcriptional regulators that are involved in inducing and enforcing various differentiation states that might limit tumor progression. We have also studied the involvement of subpopulations of tumor cells in this model in which the Wnt signaling pathway is active. Data from lineage tracing experiments are consistent with this population having increased tumor propagating potential. In order to functionally characterize these and other cancer genes, we have recently developed methods to mutate genes in developing tumors using the CRISPR/Cas9 system. Using tri-functional lentiviral, we have developed tumors that have undergone Cas9-mediated mutation of a series of genes of interest. Several examples of the application of this method will be reviewed. As another example of cancer genome manipulation, we have engineered tumors of the lung and other sites to express specific T cell antigens and/or NK cell ligands. These models have allowed a detailed investigation of adaptive and innate immune responses to a developing tumor. We have used the models to explore mechanisms of immune suppression in cancer and develop methods to elicit improved anti-tumor immune responses. Disclosures No relevant conflicts of interest to declare.

scholarly journals Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Bi-Qing Li ◽  
Jin You ◽  
Lei Chen ◽  
Jian Zhang ◽  
Ning Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Shortest Path ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Shortest Paths ◽  
Gene Expression Profiles ◽  
Cancer Genes ◽  
Ppi Network ◽  
Protein Protein Interaction

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutationPvalue less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

scholarly journals Selective pressures on human cancer genes along the evolution of mammals

2018 ◽  
Author(s):  
Alberto Vicens ◽  
David Posada
Keyword(s):  
Positive Selection ◽  
Hereditary Cancer ◽  
Clonal Selection ◽  
Human Cancer ◽  
Cancer Genes ◽  
Pathogenic Variants ◽  
Potential Association ◽  
Show Evidence ◽  
Mammalian Genomes ◽  
Evolution Of Mammals

AbstractCancer is a disease of the genome caused by somatic mutation and subsequent clonal selection. Several genes associated to cancer in humans, hereafter cancer genes, also show evidence of (germline) positive selection among species. Taking advantage of a large collection of mammalian genomes, we systematically looked for statistically significant signatures of positive selection using dN/dS models in a list of 430 cancer genes. Among these, we identified 63 genes under putative positive selection in mammals, which are significantly enriched in processes like crosslinking DNA repair. We also found evidence of a higher incidence of positive selection in cancer genes bearing germline mutations, like BRCA2, where positively selected residues are physically linked with known pathogenic variants, suggesting a potential association between germline positive selection and risk of hereditary cancer. Overall, our results suggest that genes associated with hereditary cancer have less selective constraints than genes related to sporadic cancer. Also, that the adaptive evolution of human cancer genes in mammals has been most likely driven by adaptive changes in important traits not directly related to cancer.

scholarly journals Selective Pressures on Human Cancer Genes along the Evolution of Mammals

Genes ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 582 ◽  
Author(s):  
Alberto Vicens ◽  
David Posada
Keyword(s):  
Positive Selection ◽  
Cancer Susceptibility ◽  
Human Cancer ◽  
Synonymous Site ◽  
Fanconi Anaemia ◽  
Cancer Genes ◽  
Synonymous Substitutions ◽  
Recessive Mutations ◽  
Show Evidence ◽  
Positively Selected Sites

Cancer is a disease driven by both somatic mutations that increase survival and proliferation of cell lineages and the evolution of genes associated with cancer risk in populations. Several genes associated with cancer in humans, hereafter cancer genes, show evidence of germline positive selection among species. Taking advantage of a large collection of mammalian genomes, we systematically looked for signatures of germline positive selection in 430 cancer genes available in COSMIC. We identified 40 cancer genes with a robust signal of positive selection in mammals. We found evidence for fewer selective constraints—higher number of non-synonymous substitutions per non-synonymous site to the number of synonymous substitutions per synonymous site (dN/dS)—and higher incidence of positive selection—more positively selected sites—in cancer genes bearing germline and recessive mutations that predispose to cancer. This finding suggests a potential association between relaxed selection, positive selection, and risk of hereditary cancer. On the other hand, we did not find significant differences in terms of tissue or gene type. Human cancer genes under germline positive selection in mammals are significantly enriched in the processes of DNA repair, with high presence of Fanconi anaemia/Breast Cancer A (FA/BRCA) pathway components and T cell proliferation genes. We also show that the inferred positively selected sites in the two genes with the strongest signal of positive selection, i.e., BRCA2 and PTPRC, are in regions of functional relevance, which could be relevant to cancer susceptibility.

scholarly journals Diagnosis of Lung Cancer by Fractal Analysis of Damaged DNA

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Hamidreza Namazi ◽  
Mona Kiminezhadmalaie
Keyword(s):  
Lung Cancer ◽  
Dna Sequences ◽  
Hurst Exponent ◽  
The Body ◽  
Cancer Genes ◽  
A Genome ◽  
Different Types ◽  
Diagnosis Of Lung Cancer ◽  
Dna Walk ◽  
Damaged Dna

Cancer starts when cells in a part of the body start to grow out of control. In fact cells become cancer cells because of DNA damage. A DNA walk of a genome represents how the frequency of each nucleotide of a pairing nucleotide couple changes locally. In this research in order to study the cancer genes, DNA walk plots of genomes of patients with lung cancer were generated using a program written in MATLAB language. The data so obtained was checked for fractal property by computing the fractal dimension using a program written in MATLAB. Also, the correlation of damaged DNA was studied using the Hurst exponent measure. We have found that the damaged DNA sequences are exhibiting higher degree of fractality and less correlation compared with normal DNA sequences. So we confirmed this method can be used for early detection of lung cancer. The method introduced in this research not only is useful for diagnosis of lung cancer but also can be applied for detection and growth analysis of different types of cancers.

scholarly journals OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers

2018 ◽  
Author(s):  
Claudia Arnedo-Pac ◽  
Loris Mularoni ◽  
Ferran Muiños ◽  
Abel Gonzalez-Perez ◽  
Nuria Lopez-Bigas
Keyword(s):  
Positive Selection ◽  
Clustering Algorithm ◽  
Source Code ◽  
Cancer Genes ◽  
Driver Genes ◽  
Local Background ◽  
Nucleotide Context ◽  
Bona Fide ◽  
Cancer Drivers ◽  
Genomic Regions

AbstractSummaryThe identification of the genomic alterations driving tumorigenesis is one of the main goals in oncogenomics research. Given the evolutionary principles of cancer development, computational methods that detect signals of positive selection in the pattern of tumor mutations have been effectively applied in the search for cancer genes. One of these signals is the abnormal clustering of mutations, which has been shown to be complementary to other signals in the detection of driver genes. We have developed OncodriveCLUSTL, a new sequence-based clustering algorithm to detect significant clustering signals across genomic regions. OncodriveCLUSTL is based on a local background model derived from the simulation of mutations accounting for the composition of tri- or penta-nucleotide context substitutions observed in the cohort under study. Our method is able to identify known clusters and bona-fide cancer drivers across cohorts of tumor whole-exomes, outperforming the existing OncodriveCLUST algorithm and complementing other methods based on different signals of positive selection. We show that OncodriveCLUSTL may be applied to the analysis of non-coding genomic elements and non-human mutations data.Availability and implementationOncodriveCLUSTL is available as an installable Python 3.5 package. The source code and running examples are freely available at https://bitbucket.org/bbglab/oncodriveclustl under GNU Affero General Public [email protected]

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