Comprehensive network analysis of different subtypes of molecular disorders in lung cancer.
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ABSTRACT:
SUBMITTER: Zhang F
PROVIDER: S-EPMC8430083 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Breast cancer is the most common invasive cancer in women worldwide, and can be subdivided into Luminal A, Luminal B, Her2, and Basal subtype (the PAM50 subtyping system). The lmo2 gene was traditionally recognized as a proto-oncogene in hematopoietic system but its functions in breast cancers remained largely unclear. Based on the Cancer Genome Atlas (TCGA) breast cancer dataset, herein we found that the significantly LMO2-correlated genes in normal or malignant samples were enriched in rather divergent cellular pathways, suggesting the function complexity of LMO2 in breast tissues. Moreover, high LMO2 expression level was found to predict a shorter patient survival in Luminal A type whereas a better outcome in Her2 type. Correspondingly, LMO2 also revealed function diversities in different PAM50 subtypes. In Luminal A type, the LMO2 related genes were primarily enriched in cancer-promoting pathways, including VEGF production, stemness, PPAR signal pathways, MAPK cascade and cell cycle regulation. In Her2 type however, the LMO2 related genes lacked the enrichment on most of the generally cancer-related pathways and were particularly enriched in negative regulation of ErbB pathway as well as MAPK cascade, suggesting a potentially anti-oncogenic role of LMO2 on this subtype. Taken together, this study drew a comprehensive overview of divergent functions of LMO2 on breast cancers, provided additional evidence for the function complexity of LMO2 in solid tumors and suggested the potential usage of LMO2 as a PAM50 subtype dependent biomarker for breast cancer clinic in the precision medicine era.
| S-EPMC5823636 | biostudies-literature
Project description:BackgroundRecent studies have reported the predictive and prognostic value of novel transcriptional factor-based molecular subtypes in small-cell lung cancer (SCLC). We conducted an in-depth analysis pairing multi-omics data with immunohistochemistry (IHC) to elucidate the underlying characteristics associated with differences in clinical outcomes between subtypes.MethodsIHC (n = 252), target exome sequencing (n = 422), and whole transcriptome sequencing (WTS, n = 189) data generated from 427 patients (86.4% males, 13.6% females) with SCLC were comprehensively analysed. The differences in the mutation profile, gene expression profile, and inflammed signatures were analysed according to the IHC-based molecular subtype.FindingsIHC-based molecular subtyping, comprised of 90 limited-disease (35.7%) and 162 extensive-disease (64.3%), revealed a high incidence of ASCL1 subtype (IHC-A, 56.3%) followed by ASCL1/NEUROD1 co-expressed (IHC-AN, 17.9%), NEUROD1 (IHC-N, 12.3%), POU2F3 (IHC-P, 9.1%), triple-negative (IHC-TN, 4.4%) subtypes. IHC-based subtype showing high concordance with WTS-based subtyping and non-negative matrix factorization (NMF) clusterization method. IHC-AN subtype resembled IHC-A (rather than IHC-N) in terms of both gene expression profiles and clinical outcomes. Favourable median overall survival was observed in IHC-A (15.2 months) compared to IHC-N (8.0 months, adjusted HR 2.3, 95% CI 1.4-3.9, p = 0.002) and IHC-P (8.3 months, adjusted HR 1.7, 95% CI 0.9-3.2, p = 0.076). Inflamed tumours made up 25% of cases (including 53% of IHC-P, 26% of IHC-A, 17% of IHC-AN, but only 11% of IHC-N). Consistent with recent findings, inflamed tumours were more likely to benefit from first-line immunotherapy treatment than non-inflamed phenotype (p = 0.002).InterpretationThis study provides fundamental data, including the incidence and basic demographics of molecular subtypes of SCLC using both IHC and WTS from a comparably large, real-world Asian/non-Western patient cohort, showing high concordance with the previous NMF-based SCLC model. In addition, we revealed underlying biological pathway activities, immunogenicity, and treatment outcomes based on molecular subtype, possibly related to the difference in clinical outcomes, including immunotherapy response.FundingThis work was supported by AstraZeneca, Future Medicine 2030 Project of the Samsung Medical Center [grant number SMX1240011], the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [grant number 2020R1C1C1010626] and the 7th AstraZeneca-KHIDI (Korea Health Industry Development Institute) oncology research program.
| S-EPMC10959651 | biostudies-literature
Project description:Oncogenes that occur in ≤5% of non-small-cell lung cancers have been defined as 'rare'; nonetheless, this frequency can correspond to a substantial number of patients diagnosed annually. Within rare oncogenes, less commonly identified alterations (such as HRAS, NRAS, RIT1, ARAF, RAF1 and MAP2K1 mutations, or ERBB family, LTK and RASGRF1 fusions) can share certain structural or oncogenic features with more commonly recognized alterations (such as KRAS, BRAF, MET and ERBB family mutations, or ALK, RET and ROS1 fusions). Over the past 5 years, a surge in the identification of rare-oncogene-driven lung cancers has challenged the boundaries of traditional clinical grade diagnostic assays and profiling algorithms. In tandem, the number of approved targeted therapies for patients with rare molecular subtypes of lung cancer has risen dramatically. Rational drug design has iteratively improved the quality of small-molecule therapeutic agents and introduced a wave of antibody-based therapeutics, expanding the list of actionable de novo and resistance alterations in lung cancer. Getting additional molecularly tailored therapeutics approved for rare-oncogene-driven lung cancers in a larger range of countries will require ongoing stakeholder cooperation. Patient advocates, health-care agencies, investigators and companies with an interest in diagnostics, therapeutics and real-world evidence have already taken steps to surmount the challenges associated with research into low-frequency drivers.
| S-EPMC10413877 | biostudies-literature
Project description:Breast cancer is a heterogeneous and complex disease, a clear manifestation of this is its classification into different molecular subtypes. On the other hand, gene transcriptional networks may exhibit different modular structures that can be related to known biological processes. Thus, modular structures in transcriptional networks may be seen as manifestations of regulatory structures that tightly controls biological processes. In this work, we identify modular structures on gene transcriptional networks previously inferred from microarray data of molecular subtypes of breast cancer: luminal A, luminal B, basal, and HER2-enriched. We analyzed the modules (communities) found in each network to identify particular biological functions (described in the Gene Ontology database) associated to them. We further explored these modules and their associated functions to identify common and unique features that could allow a better level of description of breast cancer, particularly in the basal-like subtype, the most aggressive and poor prognosis manifestation. Our findings related to the immune system and a decrease in cell death-related processes in basal subtype could help to understand it and design strategies for its treatment.
| S-EPMC5699328 | biostudies-literature
Project description:BackgroundTranscriptional networks play a central role in cancer development. The authors described a systems biology approach to cancer classification based on the reverse engineering of the transcriptional network surrounding the 2 most common types of lung cancer: adenocarcinoma (AC) and squamous cell carcinoma (SCC).MethodsA transcriptional network classifier was inferred from the molecular profiles of 111 human lung carcinomas. The authors tested its classification accuracy in 7 independent cohorts, for a total of 422 subjects of Caucasian, African, and Asian descent.ResultsThe model for distinguishing AC from SCC was a 25-gene network signature. Its performance on the 7 independent cohorts achieved 95.2% classification accuracy. Even more surprisingly, 95% of this accuracy was explained by the interplay of 3 genes (KRT6A, KRT6B, KRT6C) on a narrow cytoband of chromosome 12. The role of this chromosomal region in distinguishing AC and SCC was further confirmed by the analysis of another group of 28 independent subjects assayed by DNA copy number changes. The copy number variations of bands 12q12, 12q13, and 12q12-13 discriminated these samples with 84% accuracy.ConclusionsThese results suggest the existence of a robust signature localized in a relatively small area of the genome, and show the clinical potential of reverse engineering transcriptional networks from molecular profiles.
| S-EPMC3017227 | biostudies-literature
Project description:Triple-negative breast cancer (TNBC) is a highly heterogeneous group of cancers, and molecular subtyping is necessary to better identify molecular-based therapies. While some classifiers have been established, no one has integrated the expression profiles of long noncoding RNAs (lncRNAs) into such subtyping criterions. Considering the emerging important role of lncRNAs in cellular processes, a novel classification integrating transcriptome profiles of both messenger RNA (mRNA) and lncRNA would help us better understand the heterogeneity of TNBC.Using human transcriptome microarrays, we analyzed the transcriptome profiles of 165 TNBC samples. We used k-means clustering and empirical cumulative distribution function to determine optimal number of TNBC subtypes. Gene Ontology (GO) and pathway analyses were applied to determine the main function of the subtype-specific genes and pathways. We conducted co-expression network analyses to identify interactions between mRNAs and lncRNAs.All of the 165 TNBC tumors were classified into four distinct clusters, including an immunomodulatory subtype (IM), a luminal androgen receptor subtype (LAR), a mesenchymal-like subtype (MES) and a basal-like and immune suppressed (BLIS) subtype. The IM subtype had high expressions of immune cell signaling and cytokine signaling genes. The LAR subtype was characterized by androgen receptor signaling. The MES subtype was enriched with growth factor signaling pathways. The BLIS subtype was characterized by down-regulation of immune response genes, activation of cell cycle, and DNA repair. Patients in this subtype experienced worse recurrence-free survival than others (log rank test, P = 0.045). Subtype-specific lncRNAs were identified, and their possible biological functions were predicted using co-expression network analyses.We developed a novel TNBC classification system integrating the expression profiles of both mRNAs and lncRNAs and determined subtype-specific lncRNAs that are potential biomarkers and targets. If further validated in a larger population, our novel classification system could facilitate patient counseling and individualize treatment of TNBC.
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Project description:Although competing endogenous RNAs (ceRNAs) have been implicated in many solid tumors, their roles in breast cancer subtypes are not well understood. We therefore generated a ceRNA network for each subtype based on the significance of both, positive co-expression and the shared miRNAs, in the corresponding subtype miRNA dys-regulatory network, which was constructed based on negative regulations between differentially expressed miRNAs and targets. All four subtype ceRNA networks exhibited scale-free architecture and showed that the common ceRNAs were at the core of the networks. Furthermore, the common ceRNA hubs had greater connectivity than the subtype-specific hubs. Functional analysis of the common subtype ceRNA hubs highlighted factors involved in proliferation, MAPK signaling pathways and tube morphogenesis. Subtype-specific ceRNA hubs highlighted unique subtype-specific pathways, like the estrogen response and inflammatory pathways in the luminal subtypes or the factors involved in the coagulation process that participates in the basal-like subtype. Ultimately, we identified 29 critical subtype-specific ceRNA hubs that characterized the different breast cancer subtypes. Our study thus provides new insight into the common and specific subtype ceRNA interactions that define the different categories of breast cancer and enhances our understanding of the pathology underlying the different breast cancer subtypes, which can have prognostic and therapeutic implications in the future.
| S-EPMC5354650 | biostudies-literature
Project description:Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.
| S-EPMC5118907 | biostudies-literature
Project description:AbstractPancreatic cancer has a very high mortality with a 5-year survival of <5%. The purpose of this study was to classify specific molecular subtypes associated with prognosis of pancreatic cancer using The Cancer Genome Atlas (TCGA) multiplatform genomic data.Multiplatform genomic data (N = 178), including gene expression, copy number alteration, and somatic mutation data, were obtained from cancer browser (https://genome-cancer.ucsc.edu, cohort: TCGA Pancreatic Cancer). Clinical data including survival results were analyzed. We also used validation cohort (GSE50827) to confirm the robustness of these molecular subtypes in pancreatic cancer.When we performed unsupervised clustering using TCGA gene expression data, we found three distinct molecular subtypes associated with different survival results. Copy number alteration and somatic mutation data showed different genomic patterns for these three subtypes. Ingenuity pathway analysis revealed that each subtype showed differentially altered pathways. Using each subtype-specific genes (200 were selected), we could predict molecular subtype in another cohort, confirming the robustness of these molecular subtypes of pancreatic cancer. Cox regression analysis revealed that molecular subtype is the only significant prognostic factor for pancreatic cancer (P = .042, 95% confidence interval 0.523-0.98).Genomic analysis of pancreatic cancer revealed 3 distinct molecular subtypes associated with different survival results. Using these subtype-specific genes and prediction model, we could predict molecular subtype associated with prognosis of pancreatic cancer.
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Project description:Whether p53, either wild type (WT) or mutant, plays cell-specific or uniform role remains controversial. Using The Cancer Genome Atlas, we examined p53 in the lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), two lung cancers with different cellular origins and frequent p53 mutation (52% and 83%, respectively). Mutant p53 more strongly correlates with different genomic alteration and protein expression profiles in LUAD than in LUSC. p53 mutation in LUAD and LUSC is associated with multiple exacerbated clinical outcomes. Although the presence of p53 mutation does not change the survival of LUAD patients, LUSC patients containing p53 mutation exhibit surprisingly prolonged survivals. Ingenuity Pathway Analyses with genes co-expressed with WT or mutant p53 in both LUAD and LUSC show that mutant p53 in these two cancers are correlated with different signaling. Additionally, WT p53 in LUAD are largely associated with activation of tumor suppressive pathways and suppression of the tumor promotive ones, a pattern different from what is observed for WT p53 in LUSC. Furthermore, pathway analyses of genes differentially expressed between cancers with mutant and WT p53 for both LUAD and LUSC revealed different pathway fashions for these two cancers. Our study indicates that both WT and mutant p53 may have cell-specific functions, which needs to be validated with future experimental investigations.
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