Proteomic Profiling of Infiltrating Ductal Carcinoma Reveals Increased Cellular Interactions with Tissue Microenvironment

Progression of invasive carcinoma involves the deregulation of molecular signaling pathways that results in the acquisition of oncogenic phenotypes. Functional enrichment analysis allows for the identification of deregulated pathways from omics scale expression data. Given the importance of post-transcriptional regulatory mechanisms on protein expression and function, identification of deregulated pathways on the basis of protein expression data is likely to provide new insights. In this study, we have developed methods for label-based mass spectrometry in a large number of samples and applied these methods toward identification and quantification of protein expression in samples of infiltrating ductal carcinoma, benign breast growths, and normal adjacent tissue. We identified 265 proteins with differential expression patterns in infiltrating ductal carcinoma relative to benign growths or normal breast tissue. Analysis of the differentially expressed proteins indicated the deregulation of signaling pathways related to proliferation, invasion and metastasis, and immune response. Our approach provides complementary information to gene expression microarray data and identifies a number of deregulated molecular signaling pathways indicative of breast cancer progression that may enable more accurate, biologically relevant diagnoses and provide a stepping stone to personalized treatment

Proteomic Profiling of Infiltrating Ductal Carcinoma Reveals Increased Cellular Interactions with Tissue Microenvironment

Progression of invasive carcinoma involves the deregulation of molecular signaling pathways that results in the acquisition of oncogenic phenotypes. Functional enrichment analysis allows for the identification of deregulated pathways from omics scale expression data. Given the importance of post-transcriptional regulatory mechanisms on protein expression and function, identification of deregulated pathways on the basis of protein expression data is likely to provide new insights. In this study, we have developed methods for label-based mass spectrometry in a large number of samples and applied these methods toward identification and quantification of protein expression in samples of infiltrating ductal carcinoma, benign breast growths, and normal adjacent tissue. We identified 265 proteins with differential expression patterns in infiltrating ductal carcinoma relative to benign growths or normal breast tissue. Analysis of the differentially expressed proteins indicated the deregulation of signaling pathways related to proliferation, invasion and metastasis, and immune response. Our approach provides complementary information to gene expression microarray data and identifies a number of deregulated molecular signaling pathways indicative of breast cancer progression that may enable more accurate, biologically relevant diagnoses and provide a stepping stone to personalized treatment

Image_6_A systematic review and meta-analysis comparing the diagnostic capability of automated breast ultrasound and contrast-enhanced ultrasound in breast cancer.png

ObjectiveTo compare the diagnostic performance of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in breast cancer.MethodsPublished studies were collected by systematically searching the databases PubMed, Embase, Cochrane Library and Web of Science. The sensitivities, specificities, likelihood ratios and diagnostic odds ratio (DOR) were confirmed. The symmetric receiver operator characteristic curve (SROC) was used to assess the threshold of ABUS and CEUS. Fagan’s nomogram was drawn. Meta-regression and subgroup analyses were applied to search for sources of heterogeneity among the included studies.ResultsA total of 16 studies were included, comprising 4115 participants. The combined sensitivity of ABUS was 0.88 [95% CI (0.73–0.95)], specificity was 0.93 [95% CI (0.82–0.97)], area under the SROC curve (AUC) was 0.96 [95% CI (0.94–0.96)] and DOR was 89. The combined sensitivity of CEUS was 0.88 [95% CI (0.84–0.91)], specificity was 0.76 [95% CI (0.66–0.84)], AUC was 0.89 [95% CI (0.86–0.92)] and DOR was 24. The Deeks’ funnel plot showed no existing publication bias. The prospective design, partial verification bias and blinding contributed to the heterogeneity in specificity, while no sources contributed to the heterogeneity in sensitivity. The post-test probability of ABUS in BC was 75%, and the post-test probability of CEUS in breast cancer was 48%.ConclusionCompared with CEUS, ABUS showed higher specificity and DOR for detecting breast cancer. ABUS is expected to further improve the accuracy of BC diagnosis.</p

Image_2_A systematic review and meta-analysis comparing the diagnostic capability of automated breast ultrasound and contrast-enhanced ultrasound in breast cancer.png

ObjectiveTo compare the diagnostic performance of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in breast cancer.MethodsPublished studies were collected by systematically searching the databases PubMed, Embase, Cochrane Library and Web of Science. The sensitivities, specificities, likelihood ratios and diagnostic odds ratio (DOR) were confirmed. The symmetric receiver operator characteristic curve (SROC) was used to assess the threshold of ABUS and CEUS. Fagan’s nomogram was drawn. Meta-regression and subgroup analyses were applied to search for sources of heterogeneity among the included studies.ResultsA total of 16 studies were included, comprising 4115 participants. The combined sensitivity of ABUS was 0.88 [95% CI (0.73–0.95)], specificity was 0.93 [95% CI (0.82–0.97)], area under the SROC curve (AUC) was 0.96 [95% CI (0.94–0.96)] and DOR was 89. The combined sensitivity of CEUS was 0.88 [95% CI (0.84–0.91)], specificity was 0.76 [95% CI (0.66–0.84)], AUC was 0.89 [95% CI (0.86–0.92)] and DOR was 24. The Deeks’ funnel plot showed no existing publication bias. The prospective design, partial verification bias and blinding contributed to the heterogeneity in specificity, while no sources contributed to the heterogeneity in sensitivity. The post-test probability of ABUS in BC was 75%, and the post-test probability of CEUS in breast cancer was 48%.ConclusionCompared with CEUS, ABUS showed higher specificity and DOR for detecting breast cancer. ABUS is expected to further improve the accuracy of BC diagnosis.</p

Image_1_A systematic review and meta-analysis comparing the diagnostic capability of automated breast ultrasound and contrast-enhanced ultrasound in breast cancer.png

ObjectiveTo compare the diagnostic performance of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in breast cancer.MethodsPublished studies were collected by systematically searching the databases PubMed, Embase, Cochrane Library and Web of Science. The sensitivities, specificities, likelihood ratios and diagnostic odds ratio (DOR) were confirmed. The symmetric receiver operator characteristic curve (SROC) was used to assess the threshold of ABUS and CEUS. Fagan’s nomogram was drawn. Meta-regression and subgroup analyses were applied to search for sources of heterogeneity among the included studies.ResultsA total of 16 studies were included, comprising 4115 participants. The combined sensitivity of ABUS was 0.88 [95% CI (0.73–0.95)], specificity was 0.93 [95% CI (0.82–0.97)], area under the SROC curve (AUC) was 0.96 [95% CI (0.94–0.96)] and DOR was 89. The combined sensitivity of CEUS was 0.88 [95% CI (0.84–0.91)], specificity was 0.76 [95% CI (0.66–0.84)], AUC was 0.89 [95% CI (0.86–0.92)] and DOR was 24. The Deeks’ funnel plot showed no existing publication bias. The prospective design, partial verification bias and blinding contributed to the heterogeneity in specificity, while no sources contributed to the heterogeneity in sensitivity. The post-test probability of ABUS in BC was 75%, and the post-test probability of CEUS in breast cancer was 48%.ConclusionCompared with CEUS, ABUS showed higher specificity and DOR for detecting breast cancer. ABUS is expected to further improve the accuracy of BC diagnosis.</p

Image_4_A systematic review and meta-analysis comparing the diagnostic capability of automated breast ultrasound and contrast-enhanced ultrasound in breast cancer.png

ObjectiveTo compare the diagnostic performance of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in breast cancer.MethodsPublished studies were collected by systematically searching the databases PubMed, Embase, Cochrane Library and Web of Science. The sensitivities, specificities, likelihood ratios and diagnostic odds ratio (DOR) were confirmed. The symmetric receiver operator characteristic curve (SROC) was used to assess the threshold of ABUS and CEUS. Fagan’s nomogram was drawn. Meta-regression and subgroup analyses were applied to search for sources of heterogeneity among the included studies.ResultsA total of 16 studies were included, comprising 4115 participants. The combined sensitivity of ABUS was 0.88 [95% CI (0.73–0.95)], specificity was 0.93 [95% CI (0.82–0.97)], area under the SROC curve (AUC) was 0.96 [95% CI (0.94–0.96)] and DOR was 89. The combined sensitivity of CEUS was 0.88 [95% CI (0.84–0.91)], specificity was 0.76 [95% CI (0.66–0.84)], AUC was 0.89 [95% CI (0.86–0.92)] and DOR was 24. The Deeks’ funnel plot showed no existing publication bias. The prospective design, partial verification bias and blinding contributed to the heterogeneity in specificity, while no sources contributed to the heterogeneity in sensitivity. The post-test probability of ABUS in BC was 75%, and the post-test probability of CEUS in breast cancer was 48%.ConclusionCompared with CEUS, ABUS showed higher specificity and DOR for detecting breast cancer. ABUS is expected to further improve the accuracy of BC diagnosis.</p

Image_3_A systematic review and meta-analysis comparing the diagnostic capability of automated breast ultrasound and contrast-enhanced ultrasound in breast cancer.jpeg

ObjectiveTo compare the diagnostic performance of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in breast cancer.MethodsPublished studies were collected by systematically searching the databases PubMed, Embase, Cochrane Library and Web of Science. The sensitivities, specificities, likelihood ratios and diagnostic odds ratio (DOR) were confirmed. The symmetric receiver operator characteristic curve (SROC) was used to assess the threshold of ABUS and CEUS. Fagan’s nomogram was drawn. Meta-regression and subgroup analyses were applied to search for sources of heterogeneity among the included studies.ResultsA total of 16 studies were included, comprising 4115 participants. The combined sensitivity of ABUS was 0.88 [95% CI (0.73–0.95)], specificity was 0.93 [95% CI (0.82–0.97)], area under the SROC curve (AUC) was 0.96 [95% CI (0.94–0.96)] and DOR was 89. The combined sensitivity of CEUS was 0.88 [95% CI (0.84–0.91)], specificity was 0.76 [95% CI (0.66–0.84)], AUC was 0.89 [95% CI (0.86–0.92)] and DOR was 24. The Deeks’ funnel plot showed no existing publication bias. The prospective design, partial verification bias and blinding contributed to the heterogeneity in specificity, while no sources contributed to the heterogeneity in sensitivity. The post-test probability of ABUS in BC was 75%, and the post-test probability of CEUS in breast cancer was 48%.ConclusionCompared with CEUS, ABUS showed higher specificity and DOR for detecting breast cancer. ABUS is expected to further improve the accuracy of BC diagnosis.</p

Proteomic Profiling of Infiltrating Ductal Carcinoma Reveals Increased Cellular Interactions with Tissue Microenvironment

Progression of invasive carcinoma involves the deregulation of molecular signaling pathways that results in the acquisition of oncogenic phenotypes. Functional enrichment analysis allows for the identification of deregulated pathways from omics scale expression data. Given the importance of post-transcriptional regulatory mechanisms on protein expression and function, identification of deregulated pathways on the basis of protein expression data is likely to provide new insights. In this study, we have developed methods for label-based mass spectrometry in a large number of samples and applied these methods toward identification and quantification of protein expression in samples of infiltrating ductal carcinoma, benign breast growths, and normal adjacent tissue. We identified 265 proteins with differential expression patterns in infiltrating ductal carcinoma relative to benign growths or normal breast tissue. Analysis of the differentially expressed proteins indicated the deregulation of signaling pathways related to proliferation, invasion and metastasis, and immune response. Our approach provides complementary information to gene expression microarray data and identifies a number of deregulated molecular signaling pathways indicative of breast cancer progression that may enable more accurate, biologically relevant diagnoses and provide a stepping stone to personalized treatment

Proteomic Profiling of Infiltrating Ductal Carcinoma Reveals Increased Cellular Interactions with Tissue Microenvironment

Progression of invasive carcinoma involves the deregulation of molecular signaling pathways that results in the acquisition of oncogenic phenotypes. Functional enrichment analysis allows for the identification of deregulated pathways from omics scale expression data. Given the importance of post-transcriptional regulatory mechanisms on protein expression and function, identification of deregulated pathways on the basis of protein expression data is likely to provide new insights. In this study, we have developed methods for label-based mass spectrometry in a large number of samples and applied these methods toward identification and quantification of protein expression in samples of infiltrating ductal carcinoma, benign breast growths, and normal adjacent tissue. We identified 265 proteins with differential expression patterns in infiltrating ductal carcinoma relative to benign growths or normal breast tissue. Analysis of the differentially expressed proteins indicated the deregulation of signaling pathways related to proliferation, invasion and metastasis, and immune response. Our approach provides complementary information to gene expression microarray data and identifies a number of deregulated molecular signaling pathways indicative of breast cancer progression that may enable more accurate, biologically relevant diagnoses and provide a stepping stone to personalized treatment

Image_5_A systematic review and meta-analysis comparing the diagnostic capability of automated breast ultrasound and contrast-enhanced ultrasound in breast cancer.png

ObjectiveTo compare the diagnostic performance of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in breast cancer.MethodsPublished studies were collected by systematically searching the databases PubMed, Embase, Cochrane Library and Web of Science. The sensitivities, specificities, likelihood ratios and diagnostic odds ratio (DOR) were confirmed. The symmetric receiver operator characteristic curve (SROC) was used to assess the threshold of ABUS and CEUS. Fagan’s nomogram was drawn. Meta-regression and subgroup analyses were applied to search for sources of heterogeneity among the included studies.ResultsA total of 16 studies were included, comprising 4115 participants. The combined sensitivity of ABUS was 0.88 [95% CI (0.73–0.95)], specificity was 0.93 [95% CI (0.82–0.97)], area under the SROC curve (AUC) was 0.96 [95% CI (0.94–0.96)] and DOR was 89. The combined sensitivity of CEUS was 0.88 [95% CI (0.84–0.91)], specificity was 0.76 [95% CI (0.66–0.84)], AUC was 0.89 [95% CI (0.86–0.92)] and DOR was 24. The Deeks’ funnel plot showed no existing publication bias. The prospective design, partial verification bias and blinding contributed to the heterogeneity in specificity, while no sources contributed to the heterogeneity in sensitivity. The post-test probability of ABUS in BC was 75%, and the post-test probability of CEUS in breast cancer was 48%.ConclusionCompared with CEUS, ABUS showed higher specificity and DOR for detecting breast cancer. ABUS is expected to further improve the accuracy of BC diagnosis.</p