The histologic phenotype of lung cancers is associated with transcriptomic features rather than genomic characteristics
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PROVIDER: PRJNA779915 | ENA |
REPOSITORIES: ENA
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Project description:Histology plays an essential role in therapeutic decision-making for lung cancer patients. However, the molecular determinants of lung cancer histology are largely unknown. We conducted whole-exome sequencing (WES) and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histology. A median of 68.9% of point mutations and 83% of copy number aberrations were shared between different histologic components within the same tumors. Furthermore, different histologic components within the tumors demonstrated similar subclonal architecture. On the other hand, transcriptomic profiling revealed shared pathways between the same histologic subtypes from different patients, which was supported by the analyses of the transcriptomic data from 141 cell lines and 343 lung cancers of different histologic subtypes. These data derived from mixed histologic subtypes in the setting of identical genetic background and exposure history support that the histologic fate of lung cancer cells is associated with transcriptomic features rather than the genomic profiles. The in-house clariom.s.human microarray data were analyzed using Bioconductor packages Oligo, pd.clariom.s.human, and limma following standard workflow. Gene set enrichment analysis using Hallmark dataset was carried out using fgsea Bioconductor package and the genes are pre-ranked by (signed log2FoldChange) * -log10(p-value) for all the public datasets. For the in-house microarray data, we computed the fold change between distinct histologies within the same patient and rank the genes by the fold change.
2021-11-13 | GSE188665 | GEO
Project description:Background: PTEN loss contributes to the development of many cancers and is associated with both hepatocellular carcinoma and cholangiocarcinoma. The pathogenesis of these malignancies is unclear, but they are speculated to arise from common cellular origins. We explored the influence of secondary effects, like hypoxia signaling, through co-deletion of Pten and Vhl in a murine model.Methods: We used a CreER-linked keratin 18 mouse model to conditionally delete Pten, Vhl or both, evaluating the resultant tumors by histology and gene expression microarray. A cohort of human cholangiocarcinoma samples was evaluated for relationships between HIF-1a expression and clinical outcomes.Results: Both Pten deletion genotypes developed liver tumors, but with differing phenotypes. Pten deletion alone led to large, invasive tumors with widespread hepatosteatosis. Co-deletion of Pten and Vhl resulted in low tumor burden and reduced steatosis. Microarray analysis divided mouse tumors’ respective genotypes by gene expression. This gene expression profile grouped a human tumor cohort according to histologic type with the Pten deletion signature aligning with hepatocellular carcinoma, whereas the Pten; Vhl deletion signature associated with cholangiocarcinomas. In a human cholangiocarcinoma cohort, we observed correlation between HIF-1a expression and overall survival.Conclusions: Pten deletion leads to tumor formation and steatosis in mouse livers. Co-deletion of Vhl and Pten resulted in lower tumor burden with gene expression profiling suggesting a switch from hepatocellular expression features to an expression profile more consistent with cholangiocarinoma. A possible relation between HIF-1a expression and increased overall survival in human cholangiocarcinoma suggests that hypoxia signaling influences tumor phenotype. reference x sample
2014-10-31 | E-GEOD-55874 | biostudies-arrayexpress
Project description:Histology plays an essential role in therapeutic decision-making for lung cancer patients. However, the molecular determinants of lung cancer histology are largely unknown. We conducted whole-exome sequencing (WES) and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histology.
| EGAS00001005140 | EGA
Project description:Background: PTEN loss contributes to the development of many cancers and is associated with both hepatocellular carcinoma and cholangiocarcinoma. The pathogenesis of these malignancies is unclear, but they are speculated to arise from common cellular origins. We explored the influence of secondary effects, like hypoxia signaling, through co-deletion of Pten and Vhl in a murine model.Methods: We used a CreER-linked keratin 18 mouse model to conditionally delete Pten, Vhl or both, evaluating the resultant tumors by histology and gene expression microarray. A cohort of human cholangiocarcinoma samples was evaluated for relationships between HIF-1a expression and clinical outcomes.Results: Both Pten deletion genotypes developed liver tumors, but with differing phenotypes. Pten deletion alone led to large, invasive tumors with widespread hepatosteatosis. Co-deletion of Pten and Vhl resulted in low tumor burden and reduced steatosis. Microarray analysis divided mouse tumors’ respective genotypes by gene expression. This gene expression profile grouped a human tumor cohort according to histologic type with the Pten deletion signature aligning with hepatocellular carcinoma, whereas the Pten; Vhl deletion signature associated with cholangiocarcinomas. In a human cholangiocarcinoma cohort, we observed correlation between HIF-1a expression and overall survival.Conclusions: Pten deletion leads to tumor formation and steatosis in mouse livers. Co-deletion of Vhl and Pten resulted in lower tumor burden with gene expression profiling suggesting a switch from hepatocellular expression features to an expression profile more consistent with cholangiocarinoma. A possible relation between HIF-1a expression and increased overall survival in human cholangiocarcinoma suggests that hypoxia signaling influences tumor phenotype.
2014-10-31 | GSE55874 | GEO
Project description:Predictors built from gene expression data accurately predict ER, PR, and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data cannot be used to predict tumor size or lymphatic-vascular invasion. Experiment Overall Design: Microarray data from the tumors of 129 patients were analyzed for the ability to predict biomarkers (ER, PR, HER2), histologic features (grade and lymphatic-vascular invasion), and stage-related information (tumor size and lymph node metastasis). Multiple statistical predictors were used and the prediction accuracy determined by error rates of prediction and by dimensional scaling and visualization of the states under study. Models to predict lymph node metastasis were built by combinations of molecular, histologic and anatomic features.
2008-06-14 | E-GEOD-5460 | biostudies-arrayexpress
Project description:Despite the similar histologic appearances of high-grade serous ovarian cancers (HGSOCs), clinical observations point to marked differences in their gross appearances. Some patients have numerous small nodules, whereas others have bulkier disease; some patients have widespread disease, whereas others have more localized disease. However, a systematic framework for classifying such gross morphologic differences does not exist. The implementation of a laparoscopic triage algorithm for patients with advanced HGSOC enabled us to prospectively obtain detailed video images of HGSOC that could be analyzed to identify morphologic subtypes. In this study, we aimed to develop and characterize a gross morphologic classification system for HGSOC. Specifically, we assessed whether HGSOC can be reliably divided into distinct gross morphologic subtypes and whether such subtypes have different clinical outcomes and molecular features.
2023-01-27 | PXD031518 | Pride
Project description:Background:To evaluate the association of multiparametric and multiregional MRI-features with key molecular characteristics in patients with newly-diagnosed glioblastoma. Methods:Retrospective data evaluation was approved by the local ethics committee of the University of Heidelberg (ethics approval number: S-320/2012) and informed consent was waived. Preoperative MRI-features were correlated with key molecular characteristics within a single-institutional cohort of 152 patients with newly-diagnosed glioblastoma. Preoperative MRI-features (n=31) included multiparametric (anatomical, diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast enhancing and non-enhancing FLAIR-hyperintense) information with (histogram) quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow / volume (CBF / CBV) and intratumoral susceptibility signals. Molecular characteristics determined with the Illumina Infinium HumanMethylation450 array included global DNA-methylation subgroups (e.g. mesenchymal (MES), RTK I “PGFRA”, RTK II “classic”), MGMT-promoter methylation status and hallmark copy-number-variations (EGFR-, PDGFRA-, MDM4- and CDK4-amplification; PTEN-, CDKN2A-, NF1- and RB1-loss). Univariate analyses (voxel-lesion-symptom-mapping for tumor location, Wilcoxon-test for all other MRI-features) as well as machine-learning models were applied to study the strength of association and discriminative value of MRI-features for predicting underlying molecular characteristics. Results: There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted p>0.05 each). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, both demonstrating increased nrCBV and nrCBF values (performance of these parameters, as assessed by the area under the ROC curve ranged from 63 to 69%, FDR-adjusted p<0.05, respectively). Subjecting all MRI-features to machine-learning-based classification allowed to predict EGFR amplification status and the RTK II “classic” GB subgroup with a moderate, yet significantly greater accuracy (63% for EGFR [p<0.01] and 61% for RTK II [p=0.01]) than the prediction by chance, whereas prediction accuracy for all other molecular parameters was non-significant (p>0.05, all models). Conclusions: In summary, we found univariate associations between established MRI-features and molecular characteristics, however not of sufficient strength to allow the generation of machine-learning classification models for reliable and clinically meaningful prediction of the assessed molecular characteristics in patients with newly-diagnosed glioblastoma.
2016-11-23 | GSE85539 | GEO
Project description:Background: Histologic grade in breast cancer provides clinically important prognostic information. However, 30%-60% of tumors are classified as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profi les of breast cancers and whether such profi les could be used to improve histologic grading. Methods: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identified differentially expressed genes in a training set of 64 estrogen receptor (ER)-positive tumor samples by comparing expression profiles between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to define the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan-Meier analysis. All statistical tests were two-sided. Results: We identified 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1-3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confidence interval = 2.25 to 5.78; P<.001, log-rank test). Conclusions: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value. NB: The patients coming from Uppsala Hospital have been also used in other studies as in GSE3494. You can find the common set of patients in removing the abbreviation "UPP_" from the sample names and compare the results with the "INDEX (ID)" from the GSE3494 series. Keywords: disease state analysis
2006-02-16 | GSE2990 | GEO
Project description:Copy number analyses of regionally separated intratumoral biopsies of prostate cancers. Intratumoral heterogeneity (ITH) leads to regional biases of the mutational landscape in a single tumor and may influence the single biopsy-based clinical diagnosis and treatment decision. To evaluate the extent of ITH in unifocal prostate cancers (PCAs) that had not been sought, we analyzed multiple regional biopsies from three PCAs using DNA copy number analyses. DNA copy number showed ITH including regional biases in the presentation of a well-known driver of TMPRSS2-ERG fusion. Our analyses identified a substantial level of genetic ITH in unifocal PCAs at the genomic levels, which should be taken into account for the curation of biomarkers in the clinical setting. Four intratumoral biopsies were obtained per tumor for three prostate cancers. Radical prostatectomy tissue from three patients with prostate cancers were obtained. Board-certified pathologists reviewed the hematoxylin&eosin stained sections and identified tumor-rich regions (> 80% purity). We selected four different areas for biopsy that were at least 5mm apart and were comprised of the most common Gleason pattern (the most common histologic patterns with minimal histologic differences). Copy number profiling was performed using Agilent 180K platform according to the manufacturer's protocol.
2014-01-18 | E-GEOD-54185 | biostudies-arrayexpress
Project description:Background: Since there is a predilection of some clinical and molecular features for a given tumor location, we assessed whether this can be confirmed in late-onset colorectal cancer (LOCRC). Methods: We analyzed clinical and molecular characteristics of LOCRC at different tumor locations in order to determine if there are differential phenotypes related with the location in the colon. Results: Right colon cancers showed features associated with sporadic Microsatellite Instability: predominance of female cases and BRAF mutations, and an important mucinous component. Left colon cancers developed a higher number of polyps and multiple primary CRCs, showed the strongest familial component, and had better prognosis. Rectal cancers showed a predominantly sporadic phenotype, with worse prognosis and a CpG Island Methylator Phenotype (CIMP)-High. No copy number alterations (CNAs) greater than or equal to 50% were observed in this LOCRC group, and the most recurrent alterations were losses at 5q13 and 14q11, and gains at 7q11, 7q21-q22, 19p13-p12, 19q13 and 20p11-q11. KRAS and PIK3CA were the only mutated genes showing differences according to the tumor location, mainly for right colon cancers. Conclusions: Categorizing LOCRC according to tumor location appears to be an adequate first step to resolving the heterogeneity of this subset of CRC.
2019-12-20 | GSE108166 | GEO