Dataset Information

Reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner

ABSTRACT: Our study identified metabolic features both in benign and malignant breast tumors and in invasive and non-invasive breast cancer. Combined ultrasound ABVS and a panel of differential serum metabolites could further improve the accuracy of preoperative diagnosis of breast cancer and guide surgical therapy

ORGANISM(S): Homo Sapiens

SUBMITTER: Peiyuan Yin

PROVIDER: PXD036696 | iProX | Wed Sep 14 00:00:00 BST 2022

REPOSITORIES: iProX

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Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner.

Liu Jianjun J Zhou Yang Y Liu Huiying H Ma Mengyan M Wang Fei F Liu Chang C Yuan Qihang Q Wang Hongjiang H Hou Xiukun X Yin Peiyuan P

Frontiers in oncology 20221012

Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and monitoring of postoperative recurrence are in critical demand. Automatic breast full volume scanning system (ABVS) is an emerging technology used as an alternative imaging method for breast cancer screening. Despite its improved detection rate of malignant tumors, ABVS cannot accurately stage breast cancer preoperatively in 30-40% of cas ...[more]

PMID: 36313729

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Project description:Background The dramatic increase in incidence of ductal carcinoma in situ (DCIS) associated with mammographic screening for breast cancer has given emphasis to the challenges of managing this important clinical entity. Unlike invasive breast cancer, there is no established histopathologic grading system for DCIS, nor are there biological markers of prognosis to guide clinical management. The aim of this study is to use molecular profiling to identify robust and clinically applicable indicators of DCIS malignant potential. Methods Areas of intraduct carcinoma, atypical ductal hyperplasia and benign epithelium were isolated from 46 well-characterised invasive breast cancers by laser capture microdissection. Microarray based gene expression profiling was used to identify genes differentially expressed between DCIS associated with grade 1 and grade 3 invasive carcinoma (‘grade associated genes’). The expression profile of these genes was then determined in all samples and used to define ‘molecular grade’ categories. The genomic basis of gene expression derived categories was examined by array-based comparative genomic hybridisation (CGH). Results DCIS samples could be divided into two subgroups, designated low and high molecular grade (MG) on the basis of expression at 173 grade associated oligonucleotide probes. The low MG subgroup included 21 DCIS samples of low (n=10) and intermediate (n=11) nuclear grade as well as all samples of ADH (n=4) and benign epithelium (n=7). The high MG subgroup included 27 DCIS samples of intermediate (n=7) and high (n=19) nuclear grade. Array CGH revealed distinct differences in the character and degree of genomic aberration associated with MG and the clinical significance of MG was verified by a strong correlation with survival in two independent invasive breast cancer gene expression datasets (n=295 and n=186). MG categories were strongly associated with histopathologic and biomarker features of DCIS. Using a classification tree model, DCIS MG could be accurately predicted in 44/46 (95.7%) of samples using a combination of nuclear grade and Ki67 score. Conclusions Molecular profiling indicates a binary grading scheme for DCIS that is both biologically relevant and clinically informative. The low and high MG DCIS classification could be recapitulated by a novel combination of routinely accessible features. This practical approach has potential to immediately improve clinical evaluation and management of DCIS. Keywords: Paired gene expression and CGH Paired CGH and Gene Expression on DCIS of the breast

Project description:The molecular basis of breast cancer invasion and metastasis is not well understood. Our objective was to analyze transcriptome differences between stromal and epithelial cells in normal breast tissue and invasive breast cancer to define the role stroma plays in invasion. Total RNA was isolated from epithelial and stromal cells that were laser captured from normal breast tissue (n=5) and invasive breast cancer (n=28). Gene expression was measured using Affymetrix U133A 2.0 GeneChips. Differential gene expression was evaluated and compared within a model that accounted for cell type (epithelial [E] versus stromal [S]), diagnosis (cancer [C] versus normal [N]) as well as cell type-diagnosis interactions. Compared to NE, the CE transcriptome was highly enriched with genes in proliferative, motility and ECM ontologies. Differences in CS and NS transcriptomes suggested that the ECM was being remodeled in invasive breast cancer, as genes were over-represented in ECM and proteolysis ontologies. Genes more highly expressed in CS compared to CE were primarily ECM components or were involved in the remodeling of ECM, suggesting that ECM biosynthesis and remodeling were initiated in the tumor stromal compartment. Experiment Overall Design: Breast tissue was collected at surgery from patients undergoing surgical resection for invasive breast cancer (n=28) and reduction mammoplasty (n=5). Tissue was cryopreserved in OCT and serial sections were cut using a cryostat. Sections were stained and epithelial and stromal cells were isolated by laser capture microdissection. Total RNA was isolated and target was prepared using NuGen Ribo-SPIA Ovation Technology. Gene expression was measured using Affymetrix U133A 2.0 GeneChips. Gene expression differences were evaluated using a linear model of the Robust Multichip Average gene expression statistic, a model that accounted for cell type (E and S), patient diagnosis (C and N), as well as cell type-diagnosis interactions. Data were examined to determine false discovery rate and were filtered for fold changes in gene expression and p-value.

Dataset Information

Reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner

Publications

Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner.

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