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Molecular Characterization of the Vasculature of Patients with Infiltrating Ductal Carcinoma generates a Gene Signature predictive of Breast Cancer Survival

ABSTRACT: Introduction: Targeting of the tumor vasculature has evolved into an integral part of existing standard anti-cancer therapies. However, recent pre-clinical and clinical studies have shown that the efficacy of these treatments is temporary at most and frequently followed by the renewed tumor growth, indicating that these targets may not be robust. Methods: We transcriptionally profiled laser capture microdissection isolated microvessels from tumor and matching adjacent normal tissue samples obtained from breast cancer patients diagnosed with infiltrating ductal carcinoma. Gene expression profiles were generated and analyzed using Genespring and the web-based WebGestalt bioinformatics Toolkit. Several differentially expressed genes were validated by immunohistochemistry. To further mine our data set we used a novel integrative systems biology network approach to identify a gene signature that could robustly stratify breast cancer patient populations. Results: Vascular enrichment of the LCM samples was confirmed by Q-PCR of CD31. Hierarchical two-dimensional clustering of the transcriptome data identified 219 significantly upregulated transcripts in at least 4/8 patient samples (cut-off >1.3-fold, p<0.05). Several genes AGRN, FLNA and ILR4 were selected and their protein overexpression was confirmed in the tumor tissue. Additional mining of the data set using the clinical information associated with these tissues by applying an integrative bioinformatics network method generated a 15-gene ‘Vascular-Derived Prognostic Predictor’ that is able to stratify patient populations in to high- and low- risk groups. Conclusions: The overall heterogeneity observed in the vascular gene expression patterns in the infiltrating ductal carcinoma samples poses a significant problem for these individual genes to be used as a robust vascular breast cancer specific marker or therapeutic target. Using an integrative bioinformatics network approach allowed us to identify a 15-gene ‘Vascular-Derived Prognostic Predictor’ that can robustly identify patients with an increased risk of local recurrence. Stratifying patient populations using this gene signature may lead to better patient-tailored treatment regimes. Two-color microarrays comparing laser-captured microvessels from the tumors and matching adjacent normal tissues of 8 breast cancer patients

ORGANISM(S): Homo sapiens

SUBMITTER: Nantel Andre

PROVIDER: S-ECPF-GEOD-43379 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:Introduction: Targeting of the tumor vasculature has evolved into an integral part of existing standard anti-cancer therapies. However, recent pre-clinical and clinical studies have shown that the efficacy of these treatments is temporary at most and frequently followed by the renewed tumor growth, indicating that these targets may not be robust. Methods: We transcriptionally profiled laser capture microdissection isolated microvessels from tumor and matching adjacent normal tissue samples obtained from breast cancer patients diagnosed with infiltrating ductal carcinoma. Gene expression profiles were generated and analyzed using Genespring and the web-based WebGestalt bioinformatics Toolkit. Several differentially expressed genes were validated by immunohistochemistry. To further mine our data set we used a novel integrative systems biology network approach to identify a gene signature that could robustly stratify breast cancer patient populations. Results: Vascular enrichment of the LCM samples was confirmed by Q-PCR of CD31. Hierarchical two-dimensional clustering of the transcriptome data identified 219 significantly upregulated transcripts in at least 4/8 patient samples (cut-off >1.3-fold, p<0.05). Several genes AGRN, FLNA and ILR4 were selected and their protein overexpression was confirmed in the tumor tissue. Additional mining of the data set using the clinical information associated with these tissues by applying an integrative bioinformatics network method generated a 15-gene M-bM-^@M-^XVascular-Derived Prognostic PredictorM-bM-^@M-^Y that is able to stratify patient populations in to high- and low- risk groups. Conclusions: The overall heterogeneity observed in the vascular gene expression patterns in the infiltrating ductal carcinoma samples poses a significant problem for these individual genes to be used as a robust vascular breast cancer specific marker or therapeutic target. Using an integrative bioinformatics network approach allowed us to identify a 15-gene M-bM-^@M-^XVascular-Derived Prognostic PredictorM-bM-^@M-^Y that can robustly identify patients with an increased risk of local recurrence. Stratifying patient populations using this gene signature may lead to better patient-tailored treatment regimes. Two-color microarrays comparing laser-captured microvessels from the tumors and matching adjacent normal tissues of 8 breast cancer patients

Project description:Introduction: Targeting of the tumor vasculature has evolved into an integral part of existing standard anti-cancer therapies. However, recent pre-clinical and clinical studies have shown that the efficacy of these treatments is temporary at most and frequently followed by the renewed tumor growth, indicating that these targets may not be robust. Methods: We transcriptionally profiled laser capture microdissection isolated microvessels from tumor and matching adjacent normal tissue samples obtained from breast cancer patients diagnosed with infiltrating ductal carcinoma. Gene expression profiles were generated and analyzed using Genespring and the web-based WebGestalt bioinformatics Toolkit. Several differentially expressed genes were validated by immunohistochemistry. To further mine our data set we used a novel integrative systems biology network approach to identify a gene signature that could robustly stratify breast cancer patient populations. Results: Vascular enrichment of the LCM samples was confirmed by Q-PCR of CD31. Hierarchical two-dimensional clustering of the transcriptome data identified 219 significantly upregulated transcripts in at least 4/8 patient samples (cut-off >1.3-fold, p<0.05). Several genes AGRN, FLNA and ILR4 were selected and their protein overexpression was confirmed in the tumor tissue. Additional mining of the data set using the clinical information associated with these tissues by applying an integrative bioinformatics network method generated a 15-gene ‘Vascular-Derived Prognostic Predictor’ that is able to stratify patient populations in to high- and low- risk groups. Conclusions: The overall heterogeneity observed in the vascular gene expression patterns in the infiltrating ductal carcinoma samples poses a significant problem for these individual genes to be used as a robust vascular breast cancer specific marker or therapeutic target. Using an integrative bioinformatics network approach allowed us to identify a 15-gene ‘Vascular-Derived Prognostic Predictor’ that can robustly identify patients with an increased risk of local recurrence. Stratifying patient populations using this gene signature may lead to better patient-tailored treatment regimes.

Project description:BackgroundThe rarity of metaplastic breast carcinoma (MBC) has resulted in limited sonographic data. Given the inferior prognosis of MBC compared to invasive ductal carcinoma (IDC), accurate preoperative differentiation between the two is imperative for effective treatment planning and prognostic prediction. The objective of this study was to assess the diagnostic accuracy of MBC and differentiate it from IDC by analyzing sonographic and clinicopathologic features.MethodsIn this retrospective cohort study, 197 women comprising 200 IDC lesions were enrolled between January 2012 and December 2021 and 20 women comprising 20 pure MBC lesions were enrolled between January 2019 and December 2019. A comparison was made between the sonographic and clinicopathologic characteristics of MBC and IDC.ResultsThe results indicated that patients with MBC had a higher proportion of tumor grade 3 (95.0% vs. 32.5%; P<0.001), high Ki-67 expression (100.0% vs. 75.0%; P<0.001), and the triple-negative subtype (90.0% vs. 13.0%; P<0.001) as compared to those with IDC. On ultrasound (US) findings, MBC lesions tended to have a larger size (≥5 cm: 45.0% vs. 1.5%; P<0.001), regular shape (45.0% vs. 1.5%, P<0.001), circumscribed margin (40.0% vs. 0.5%, P<0.001), a complex cystic and solid echo pattern (50.0% vs. 3.5%; P<0.001), and posterior acoustic enhancement (95.0% vs. 14.5%; P<0.001). Additionally, MBC was more likely to be misinterpreted as a benign lesion by sonographers than was IDC (30.0% vs. 4.5%; P<0.001). Multilayer perceptron analysis revealed posterior acoustic enhancement, circumscribed margins, and size as distinguishing factors between these two tumor types. The estimated rates of local recurrence, distant metastasis, and 5-year overall survival in 19 cases with MBC were found to be 10.5%, 31.6%, and 65.0%, respectively.ConclusionsMBC typically presents as a large breast mass with more benign US features in older women, findings which may facilitate its accurate diagnosis and differentiation from other breast masses.

Project description:BackgroundA major challenge in the interpretation of genomic profiling data generated from breast cancer samples is the identification of driver genes as distinct from bystander genes which do not impact tumorigenesis. One way to assess the relative importance of alterations in the transcriptome profile is to combine parallel analyses that assess changes in the copy number alterations (CNAs). This integrated analysis permits the identification of genes with altered expression that map within specific chromosomal regions which demonstrate copy number alterations, providing a mechanistic approach to identify the 'driver genes'.MethodsWe have performed whole genome analysis of CNAs using the Affymetrix 250K Mapping array on 22 infiltrating ductal carcinoma samples (IDCs). Analysis of transcript expression alterations was performed using the Affymetrix U133 Plus2.0 array on 16 IDC samples. Fourteen IDC samples were analyzed using both platforms and the data integrated. We also incorporated data from loss of heterozygosity (LOH) analysis to identify genes showing altered expression in LOH regions.ResultsCommon chromosome gains and amplifications were identified at 1q21.3, 6p21.3, 7p11.2-p12.1, 8q21.11 and 8q24.3. A novel amplicon was identified at 5p15.33. Frequent losses were found at 1p36.22, 8q23.3, 11p13, 11q23, and 22q13. Over 130 genes were identified with concurrent increases or decreases in expression that mapped to these regions of copy number alterations. LOH analysis revealed three tumors with whole chromosome or p arm allelic loss of chromosome 17. Genes were identified that mapped to copy neutral LOH regions. LOH with accompanying copy loss was detected on Xp24 and Xp25 and genes mapping to these regions with decreased expression were identified. Gene expression data highlighted the PPARα/RXRα Activation Pathway as down-regulated in the tumor samples.ConclusionWe have demonstrated the utility of the application of integrated analysis using high resolution CGH and whole genome transcript analysis for detecting driver genes in IDC. The high resolution platform allowed a refined demarcation of CNAs and gene expression profiling provided a mechanism to detect genes directly impacted by the CNA. This is the first report of LOH integrated with gene expression in IDC using a high resolution platform.

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Molecular Characterization of the Vasculature of Patients with Infiltrating Ductal Carcinoma generates a Gene Signature predictive of Breast Cancer Survival

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