Dataset Information

DEEP SEQUENCING OF MODELS OF BREAST DUCTAL CARCINOMA IN SITU REVEALS ALDH5A1 AS A NOVEL POTENTIAL THERAPEUTIC TARGET

ABSTRACT: We attempted to identify alterations in gene expression that occur during the progression from normal breast to ductal carcinoma in situ (DCIS) with the aim to elucidate significant genes and pathways underlying the premalignant transformation. To determine the expression changes that are common to multiple DCIS models (MCF10.DCIS, SUM102 and SUM225) and normal mammary epithelial cells (MCF10A), we grew the cells in three dimensional overlay culture with reconstituted basement membrane and used the extracted RNA for 76 cycles of deep sequencing (mRNA-Seq) using Illumina Genome Analyzer GAIIx. Analysis of mRNA-Seq results showed 295 consistently differentially expressed transcripts in DCIS models as compared to MCF10A. These differentially expressed genes are associated with a number of signaling pathways such as integrin, fibroblast growth factor and TGFß signaling. Many differentially expressed transcripts in DCIS were found to be involved in cell-cell signaling, cell-cell adhesion and cell proliferation. We further investigated ALDH5A1 gene that encodes for the enzyme, aldehyde dehydrogenase 5A1, which is involved in glutamate metabolism. Further, inhibition of ALDH5A1 with different pharmacological drugs resulted in significant inhibition of cell growth and proliferation in the DCIS models. Four cell lines examined: normal mammary epithelial cell line (one sample) and three ductal carcinoma in situ cell lines (three samples). Each sample has two duplicates

ORGANISM(S): Homo sapiens

SUBMITTER: MATTINGLY RAYMOND

PROVIDER: S-ECPF-GEOD-36863 | biostudies-other |

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed and evaluated a deep learning-based DCIS grading system. The system was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the three observers (o1, o2 and o3) (κo1,dl = 0.81, κo2,dl = 0.53 and κo3,dl = 0.40) than the observers amongst each other (κo1,o2 = 0.58, κo1,o3 = 0.50 and κo2,o3 = 0.42) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers (κo1,dl = 0.77, κo2,dl = 0.75 and κo3,dl = 0.70) as the observers amongst each other (κo1,o2 = 0.77, κo1,o3 = 0.75 and κo2,o3 = 0.72). The deep learning system better reflected the grading spectrum of DCIS than two of the observers. In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. To the best of our knowledge, this is the first automated system for the grading of DCIS that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.

Dataset Information

DEEP SEQUENCING OF MODELS OF BREAST DUCTAL CARCINOMA IN SITU REVEALS ALDH5A1 AS A NOVEL POTENTIAL THERAPEUTIC TARGET

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets