Combined clinical and genomic signatures for the prognosis of early stage non-small cell lung cancer based on gene copy number alterations
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE72195 | GEO | 2015/08/20
SECONDARY ACCESSION(S): PRJNA293291
REPOSITORIES: GEO
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Project description:BACKGROUND:The development of a more refined prognostic methodology for early non-small cell lung cancer (NSCLC) is an unmet clinical need. An accurate prognostic tool might help to select patients at early stages for adjuvant therapies. RESULTS:A new integrated bioinformatics searching strategy, that combines gene copy number alterations and expression, together with clinical parameters was applied to derive two prognostic genomic signatures. The proposed methodology combines data from patients with and without clinical data with a priori information on the ability of a gene to be a prognostic marker. Two initial candidate sets of 513 and 150 genes for lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), respectively, were generated by identifying genes which have both: a) significant correlation between copy number and gene expression, and b) significant prognostic value at the gene expression level in external databases. From these candidates, two panels of 7 (ADC) and 5 (SCC) genes were further identified via semi-supervised learning. These panels, together with clinical data (stage, age and sex), were used to construct the ADC and SCC hazard scores combining clinical and genomic data. The signatures were validated in two independent datasets (n?=?73 for ADC, n?=?97 for SCC), confirming that the prognostic value of both clinical-genomic models is robust, statistically significant (P?=?0.008 for ADC and P?=?0.019 for SCC) and outperforms both the clinical models (P?=?0.060 for ADC and P?=?0.121 for SCC) and the genomic models applied separately (P?=?0.350 for ADC and P?=?0.269 for SCC). CONCLUSION:The present work provides a methodology to generate a robust signature using copy number data that can be potentially used to any cancer. Using it, we found new prognostic scores based on tumor DNA that, jointly with clinical information, are able to predict overall survival (OS) in patients with early-stage ADC and SCC.
| S-EPMC4595201 | biostudies-literature
Project description:Background: The development of a more refined prognostic methodology for early non-small cell lung cancer (NSCLC) is an unmet clinical need. An accurate prognostic tool might help to select patients at early stages for adjuvant therapies. Results: A new integrated bioinformatics searching strategy, that combines gene copy number alterations and expression, together with clinical parameters was applied to derive two prognostic genomic signatures. The proposed methodology combines data from patients with and without clinical data with a priori information on the ability of a gene to be a prognostic marker. Two initial candidate sets of 513 and 150 genes for lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), respectively, were generated by identifying genes which have both: a) significant correlation between copy number and gene expression, and b) significant prognostic value at the gene expression level in external databases. From these candidates, two panels of 7 (ADC) and 5 (SCC) genes were further identified via semi-supervised learning. These panels, together with clinical data (stage, age and sex), were used to construct the ADC and SCC hazard scores combining clinical and genomic data. The signatures were validated in two independent datasets (n=73 for ADC, n=97 for SCC), confirming that the prognostic value of both clinical-genomic models is robust, statistically significant (P=0.008 for ADC and P=0.019 for SCC) and outperforms both the clinical models (P=0.060 for ADC and P=0.121 for SCC) and the genomic models applied separately (P=0.350 for ADC and P=0.269 for SCC). Conclusion: The present work provides a methodology to generate a robust signature using copy number data that can be potentially used to any cancer. Using it, we found new prognostic scores based on DNA s that, jointly with clinical information, are able to predict overall survival (OS) in patients with early-stage ADC and SCC.
2015-08-20 | GSE72192 | GEO
Project description:Background: The development of a more refined prognostic methodology for early non-small cell lung cancer (NSCLC) is an unmet clinical need. An accurate prognostic tool might help to select patients at early stages for adjuvant therapies. Results: A new integrated bioinformatics searching strategy, that combines gene copy number alterations and expression, together with clinical parameters was applied to derive two prognostic genomic signatures. The proposed methodology combines data from patients with and without clinical data with a priori information on the ability of a gene to be a prognostic marker. Two initial candidate sets of 513 and 150 genes for lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), respectively, were generated by identifying genes which have both: a) significant correlation between copy number and gene expression, and b) significant prognostic value at the gene expression level in external databases. From these candidates, two panels of 7 (ADC) and 5 (SCC) genes were further identified via semi-supervised learning. These panels, together with clinical data (stage, age and sex), were used to construct the ADC and SCC hazard scores combining clinical and genomic data. The signatures were validated in two independent datasets (n=73 for ADC, n=97 for SCC), confirming that the prognostic value of both clinical-genomic models is robust, statistically significant (P=0.008 for ADC and P=0.019 for SCC) and outperforms both the clinical models (P=0.060 for ADC and P=0.121 for SCC) and the genomic models applied separately (P=0.350 for ADC and P=0.269 for SCC). Conclusion: The present work provides a methodology to generate a robust signature using copy number data that can be potentially used to any cancer. Using it, we found new prognostic scores based on DNA s that, jointly with clinical information, are able to predict overall survival (OS) in patients with early-stage ADC and SCC.
2015-08-20 | GSE72194 | GEO
Project description:Gains and losses of DNA are prevalent in cancer and emerge as a consequence of inter-related processes of replication stress, mitotic errors, spindle multipolarity and breakage-fusion-bridge cycles, among others, which may lead to chromosomal instability and aneuploidy1,2. These copy number alterations contribute to cancer initiation, progression and therapeutic resistance3-5. Here we present a conceptual framework to examine the patterns of copy number alterations in human cancer that is widely applicable to diverse data types, including whole-genome sequencing, whole-exome sequencing, reduced representation bisulfite sequencing, single-cell DNA sequencing and SNP6 microarray data. Deploying this framework to 9,873 cancers representing 33 human cancer types from The Cancer Genome Atlas6 revealed a set of 21 copy number signatures that explain the copy number patterns of 97% of samples. Seventeen copy number signatures were attributed to biological phenomena of whole-genome doubling, aneuploidy, loss of heterozygosity, homologous recombination deficiency, chromothripsis and haploidization. The aetiologies of four copy number signatures remain unexplained. Some cancer types harbour amplicon signatures associated with extrachromosomal DNA, disease-specific survival and proto-oncogene gains such as MDM2. In contrast to base-scale mutational signatures, no copy number signature was associated with many known exogenous cancer risk factors. Our results synthesize the global landscape of copy number alterations in human cancer by revealing a diversity of mutational processes that give rise to these alterations.
| S-EPMC9242861 | biostudies-literature
Project description:ObjectivesCopy number variants (CNVs) are well-known genetic pleiotropic risk factors for multiple neurodevelopmental and psychiatric disorders (NPDs) including autism (ASD) and schizophrenia (SZ). Overall, little is known about how different CNVs conferring risk for the same condition may affect subcortical brain structures and how these alterations relate to the level of disease risk conferred by CNVs. To fill this gap, we investigated gross volume, and vertex level thickness and surface maps of subcortical structures in 11 different CNVs and 6 different NPDs.MethodsSubcortical structures were characterized using harmonized ENIGMA protocols in 675 CNV carriers (at the following loci: 1q21.1, TAR, 13q12.12, 15q11.2, 16p11.2, 16p13.11, and 22q11.2) and 782 controls (Male/Female: 727/730; age-range: 6-80 years) as well as ENIGMA summary-statistics for ASD, SZ, ADHD, Obsessive-Compulsive-Disorder, Bipolar-Disorder, and Major-Depression.ResultsNine of the 11 CNVs affected volume of at least one subcortical structure. The hippocampus and amygdala were affected by five CNVs. Effect sizes of CNVs on subcortical volume, thickness and local surface area were correlated with their previously reported effect sizes on cognition and risk for ASD and SZ. Shape analyses were able to identify subregional alterations that were averaged out in volume analyses. We identified a common latent dimension - characterized by opposing effects on basal ganglia and limbic structures - across CNVs and across NPDs.ConclusionOur findings demonstrate that subcortical alterations associated with CNVs show varying levels of similarities with those associated with neuropsychiatric conditions. We also observed distinct effects with some CNVs clustering with adult conditions while others clustered with ASD. This large cross-CNV and NPDs analysis provide insight into the long-standing questions of why CNVs at different genomic loci increase the risk for the same NPD, as well as why a single CNV increases the risk for a diverse set of NPDs.
| S-EPMC9980268 | biostudies-literature
Project description:ObjectiveCopy number variants (CNVs) are well-known genetic pleiotropic risk factors for multiple neurodevelopmental and psychiatric disorders (NPDs), including autism (ASD) and schizophrenia. Little is known about how different CNVs conferring risk for the same condition may affect subcortical brain structures and how these alterations relate to the level of disease risk conferred by CNVs. To fill this gap, the authors investigated gross volume, vertex-level thickness, and surface maps of subcortical structures in 11 CNVs and six NPDs.MethodsSubcortical structures were characterized using harmonized ENIGMA protocols in 675 CNV carriers (CNVs at 1q21.1, TAR, 13q12.12, 15q11.2, 16p11.2, 16p13.11, and 22q11.2; age range, 6-80 years; 340 males) and 782 control subjects (age range, 6-80 years; 387 males) as well as ENIGMA summary statistics for ASD, schizophrenia, attention deficit hyperactivity disorder, obsessive-compulsive disorder, bipolar disorder, and major depression.ResultsAll CNVs showed alterations in at least one subcortical measure. Each structure was affected by at least two CNVs, and the hippocampus and amygdala were affected by five. Shape analyses detected subregional alterations that were averaged out in volume analyses. A common latent dimension was identified, characterized by opposing effects on the hippocampus/amygdala and putamen/pallidum, across CNVs and across NPDs. Effect sizes of CNVs on subcortical volume, thickness, and local surface area were correlated with their previously reported effect sizes on cognition and risk for ASD and schizophrenia.ConclusionsThe findings demonstrate that subcortical alterations associated with CNVs show varying levels of similarities with those associated with neuropsychiatric conditions, as well distinct effects, with some CNVs clustering with adult-onset conditions and others with ASD. These findings provide insight into the long-standing questions of why CNVs at different genomic loci increase the risk for the same NPD and why a single CNV increases the risk for a diverse set of NPDs.
| S-EPMC10885337 | biostudies-literature
Project description:Successful treatment decisions in cancer depend on the accurate assessment of patient risk. To improve our understanding of the molecular alterations that underlie deadly malignancies, we analyzed the genomic profiles of 17,879 tumors from patients with known outcomes. We find that mutations in almost all cancer driver genes contain remarkably little information on patient prognosis. However, CNAs in these same driver genes harbor significant prognostic power. Focal CNAs are associated with worse outcomes than broad alterations, and CNAs in many driver genes remain prognostic when controlling for stage, grade, TP53 status, and total aneuploidy. By performing a meta-analysis across independent patient cohorts, we identify robust prognostic biomarkers in specific cancer types, and we demonstrate that a subset of these alterations also confer specific therapeutic vulnerabilities. In total, our analysis establishes a comprehensive resource for cancer biomarker identification and underscores the importance of gene copy number profiling in assessing clinical risk.
| S-EPMC6289580 | biostudies-literature
Project description:Duplications of the Xq28 region are the most frequent chromosomal aberrations observed in patients with intellectual disability (ID), especially in males. These duplications occur by variable mechanisms, including interstitial duplications mediated by segmental duplications in this region and terminal duplications (functional disomy) derived from translocation with other chromosomes. The most commonly duplicated region includes methyl CpG-binding protein 2 gene (MECP2), which has a minimal duplicated size of 0.2?Mb. Patients with MECP2 duplications show severe ID, intractable seizures and recurrent infections. Duplications in the telomeric neighboring regions, which include GDP dissociation inhibitor 1 gene (GDI1) and ras-associated protein RAB39B gene (RAB39B), are independently associated with ID, and many segmental duplications located in this region could mediate these frequently observed interstitial duplications. In addition, large duplications, including MECP2 and GDI1, induce hypoplasia of the corpus callosum. Abnormalities observed in the white matter, revealed by brain magnetic resonance imaging, are a common finding in patients with MECP2 duplications. As primary sequence analysis cannot be used to determine the region responsible for chromosomal duplication syndrome, finding this region relies on the collection of genotype-phenotype data from patients.
| S-EPMC4785515 | biostudies-literature
Project description:Human cancers are characterized by the presence of genomic instability. Recently, two studies have catalogued the presence of a specific class of genomic aberrations, large deletions and insertions, in a few thousand human cancers and reported that most of the prevalent recurrent focal deletions targeted common fragile sites and large genes. In various experimental systems, deletions in common fragile sites and large genes have been linked to the presence of DNA replication stress. Thus, taken together, these results suggest the presence of DNA replication stress in human cancers, consistent with the recently proposed oncogene-induced DNA damage model for cancer development.
| S-EPMC5528318 | biostudies-other
Project description:Tumor staging of upper tract urothelial carcinomas (UTUCs) is relatively difficult to assert accurately before surgery. Here, we used copy number (CN) signatures as a tool to explore their clinical significance of molecular stratification in UTUC. CN signatures were extracted by non-negative matrix factorization from the whole-genome sequencing (WGS) data of 90 Chinese UTUC primary tumor samples. A validation UTUC cohort (n = 56) and a cohort from urinary cell-free DNA (cfDNA) of urothelial cancer patients (n = 94) and matched primary tumors were also examined. Survival analyses were measured using the Kaplan-Meier, and Cox regression was used for multivariate analysis. Here, we identified six CN signatures (Sig1-6). Patients with a high contribution of Sig6 (Sig6high) were associated with higher microsatellite instability level and papillary architecture and had a favorable outcome. Patients with a low weighted genome integrity index were associated with positive lymph node and showed the worst outcome. Sig6high was identified to be an independently prognostic factor. The predictive significance of CN signature was identified by a validation UTUC cohort. CN signatures retained great concordance between primary tumor and urinary cfDNA. In conclusion, our results reveal that CN signature assessment for risk stratification is feasible and provides a basis for clinical studies that evaluate therapeutic interventions and prognosis.
| S-EPMC8427613 | biostudies-literature